SDTMIG V3.1.2 SDTM Implementation Guide

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CDISC SDTM Implementation Guide (Version 3.1.2)

Study Data Tabulation Model
Implementation Guide:
Human Clinical Trials
Prepared by the

CDISC Submission Data Standards Team

Notes to Readers



This is the implementation guide for Human Clinical Trials corresponding to Version 1.2 of the CDISC
Study Data Tabulation Model.
This Implementation Guide comprises version 3.1.2 (V3.1.2) of the CDISC Submission Data Standards
and domain models.

Revision History
Date
2008-11-12

Version
3.1.2 Final

2007-07-25
2005-08-26

3.1.2 Draft
3.1.1 Final

2004-07-14

3.1

Summary of Changes
Released version reflecting all changes and
corrections identified during comment period.
Draft for comment.
Released version reflecting all changes and
corrections identified during comment period.
Released version reflecting all changes and
corrections identified during comment periods.

Note: Please see Appendix F for Representations and Warranties, Limitations of Liability, and Disclaimers.
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CONTENTS
1

INTRODUCTION................................................................................................... 7

1.1
1.2
1.3
1.4
1.5

PURPOSE.............................................................................................................................................................7
ORGANIZATION OF THIS DOCUMENT...................................................................................................................7
RELATIONSHIP TO PRIOR CDISC DOCUMENTS ...................................................................................................8
HOW TO READ THIS IMPLEMENTATION GUIDE ....................................................................................................9
SUBMITTING COMMENTS ....................................................................................................................................9

FUNDAMENTALS OF THE SDTM ...................................................................... 10

2.1
2.2
2.3
2.4
2.5
2.6

OBSERVATIONS AND VARIABLES ....................................................................................................................... 10
DATASETS AND DOMAINS ................................................................................................................................. 11
SPECIAL-PURPOSE DATASETS ........................................................................................................................... 12
THE GENERAL OBSERVATION CLASSES ............................................................................................................. 12
THE SDTM STANDARD DOMAIN MODELS ....................................................................................................... 13
CREATING A NEW DOMAIN ............................................................................................................................... 14

SUBMITTING DATA IN STANDARD FORMAT .................................................. 16

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3.1
3.2

STANDARD METADATA FOR DATASET CONTENTS AND ATTRIBUTES.................................................................. 16
USING THE CDISC DOMAIN MODELS IN REGULATORY SUBMISSIONS — DATASET METADATA ....................... 17
3.2.1.1 Primary Keys ....................................................................................................................................... 19
3.2.1.2 CDISC Submission Value-Level Metadata .......................................................................................... 20
3.2.2
Conformance........................................................................................................................................ 20

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ASSUMPTIONS FOR DOMAIN MODELS .......................................................... 21

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4.1
GENERAL ASSUMPTIONS FOR ALL DOMAINS .................................................................................................... 21
4.1.1
General Domain Assumptions ............................................................................................................. 21
4.1.1.1 Review Study Data Tabulation and Implementation Guide ................................................................. 21
4.1.1.2 Relationship to Analysis Datasets ........................................................................................................ 21
4.1.1.3 Additional Timing Variables ................................................................................................................ 21
4.1.1.4 Order of the Variables .......................................................................................................................... 21
4.1.1.5 CDISC Core Variables ......................................................................................................................... 21
4.1.1.6 Additional Guidance on Dataset Naming ............................................................................................ 22
4.1.1.7 Splitting Domains ................................................................................................................................ 22
4.1.1.8 Origin Metadata ................................................................................................................................... 25
4.1.1.9 Assigning Natural Keys in the Metadata ............................................................................................. 26
4.1.2
General Variable Assumptions ............................................................................................................. 28
4.1.2.1 Variable-Naming Conventions ............................................................................................................. 28
4.1.2.2 Two-Character Domain Identifier ........................................................................................................ 28
4.1.2.3 Use of ―Subject‖ and USUBJID .......................................................................................................... 29
4.1.2.4 Case Use of Text in Submitted Data .................................................................................................... 29
4.1.2.5 Convention for Missing Values ............................................................................................................ 29
4.1.2.6 Grouping Variables and Categorization ............................................................................................... 29
4.1.2.7 Submitting Free Text from the CRF..................................................................................................... 31
4.1.2.8 Multiple Values for a Variable ............................................................................................................. 33
4.1.3
Coding and Controlled Terminology Assumptions .............................................................................. 35
4.1.3.1 Types of Controlled Terminology ........................................................................................................ 35
4.1.3.2 Controlled Terminology Text Case ...................................................................................................... 35
4.1.3.3 Controlled Terminology Values ........................................................................................................... 35
4.1.3.4 Use of Controlled Terminology and Arbitrary Number Codes ............................................................ 36
4.1.3.5 Storing Controlled Terminology for Synonym Qualifier Variables ..................................................... 36
4.1.3.6 Storing Topic Variables for General Domain Models .......................................................................... 36
4.1.3.7 Use of ―Yes‖ and ―No‖ Values ............................................................................................................. 36
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4.1.4
4.1.4.1
4.1.4.2
4.1.4.3
4.1.4.4
4.1.4.5
4.1.4.6
4.1.4.7
4.1.4.8
4.1.4.9
4.1.4.10
4.1.5
4.1.5.1
4.1.5.2
4.1.5.3
4.1.5.4
4.1.5.5
4.1.5.6
4.1.5.7
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Actual and Relative Time Assumptions ............................................................................................... 37
Formats for Date/Time Variables ......................................................................................................... 37
Date/Time Precision............................................................................................................................. 38
Intervals of Time and Use of Duration for --DUR Variables ............................................................... 39
Use of the ―Study Day‖ Variables ........................................................................................................ 40
Clinical Encounters and Visits ............................................................................................................. 41
Representing Additional Study Days ................................................................................................... 41
Use of Relative Timing Variables ........................................................................................................ 42
Date and Time Reported in a Domain Based on Findings ................................................................... 44
Use of Dates as Result Variables.......................................................................................................... 44
Representing Time Points .................................................................................................................... 44
Other Assumptions ............................................................................................................................... 47
Original and Standardized Results of Findings and Tests Not Done ................................................... 47
Linking of Multiple Observations ........................................................................................................ 50
Text Strings That Exceed the Maximum Length for General-Observation-Class Domain Variables .. 50
Evaluators in the Interventions and Events Observation Classes......................................................... 51
Clinical Significance for Findings Observation Class Data ................................................................. 52
Supplemental Reason Variables ........................................................................................................... 52
Presence or Absence of Pre-Specified Interventions and Events ......................................................... 52
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MODELS FOR SPECIAL-PURPOSE DOMAINS ................................................. 54

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5.1
DEMOGRAPHICS ............................................................................................................................................... 54
5.1.1
Demographics — DM .......................................................................................................................... 54
5.1.1.1 Assumptions for Demographics Domain Model.................................................................................. 56
5.1.1.2 Examples for Demographics Domain Model ....................................................................................... 57
5.2
COMMENTS....................................................................................................................................................... 64
5.2.1
Comments — CO ................................................................................................................................ 64
5.2.1.1 Assumptions for Comments Domain Model ....................................................................................... 65
5.2.1.2 Examples for Comments Domain Model ............................................................................................. 66
5.3
SUBJECT ELEMENTS AND VISITS ....................................................................................................................... 67
5.3.1
Subject Elements — SE ....................................................................................................................... 67
5.3.1.1 Assumptions for Subject Elements Domain Model ............................................................................. 68
5.3.1.2 Examples for Subject Elements Domain Model .................................................................................. 70
5.3.2
Subject Visits — SV ............................................................................................................................ 72
5.3.2.1 Assumptions for Subject Visits Domain Model ................................................................................... 73
5.3.2.2 Examples for Subject Visits Domain Model ........................................................................................ 74
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DOMAIN MODELS BASED ON THE GENERAL OBSERVATION CLASSES .... 75

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6.1
INTERVENTIONS ................................................................................................................................................ 75
6.1.1
Concomitant Medications — CM ........................................................................................................ 75
6.1.1.1 Assumptions for Concomitant Medications Domain Model................................................................ 78
6.1.1.2 Examples for Concomitant Medications Domain Model ..................................................................... 80
6.1.2
Exposure — EX ................................................................................................................................... 82
6.1.2.1 Assumptions for Exposure Domain Model .......................................................................................... 84
6.1.2.2 Examples for Exposure Domain Model ............................................................................................... 85
6.1.3
Substance Use — SU ........................................................................................................................... 89
6.1.3.1 Assumptions for Substance Use Domain Model ................................................................................. 92
6.1.3.2 Example for Substance Use Domain Model ........................................................................................ 93
6.2
EVENTS ............................................................................................................................................................ 94
6.2.1
Adverse Events — AE ......................................................................................................................... 94
6.2.1.1 Assumptions for Adverse Event Domain Model ................................................................................. 97
6.2.1.2 Examples for Adverse Events Domain Model ................................................................................... 100
6.2.2
Disposition — DS .............................................................................................................................. 103
6.2.2.1 Assumptions for Disposition Domain Model .................................................................................... 104
6.2.2.2 Examples for Disposition Domain Model ......................................................................................... 106
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6.2.3
Medical History — MH ..................................................................................................................... 110
6.2.3.1 Assumptions for Medical History Domain Model ............................................................................. 112
6.2.3.2 Examples for Medical History Domain Model .................................................................................. 114
6.2.4
Protocol Deviations — DV ................................................................................................................ 117
6.2.4.1 Assumptions for Protocol Deviations Domain Model ....................................................................... 118
6.2.4.2 Examples for Protocol Deviations Domain Model ............................................................................ 118
6.2.5
Clinical Events — CE ........................................................................................................................ 119
6.2.5.1 Assumptions for Clinical Events Domain Model .............................................................................. 121
6.2.5.2 Examples for Clinical Events Domain Model ................................................................................... 122
6.3
FINDINGS ........................................................................................................................................................ 124
6.3.1
ECG Test Results — EG .................................................................................................................... 124
6.3.1.1 Assumptions for ECG Test Results Domain Model ........................................................................... 127
6.3.1.2 Examples for ECG Test Results Domain Model ................................................................................ 127
6.3.2
Inclusion/Exclusion Criteria Not Met — IE ...................................................................................... 130
6.3.2.1 Assumptions for Inclusion/Exclusion Criteria Not Met Domain Model ........................................... 131
6.3.2.2 Examples for Inclusion/Exclusion Not Met Domain Model .............................................................. 132
6.3.3
Laboratory Test Results — LB .......................................................................................................... 133
6.3.3.1 Assumptions for Laboratory Test Results Domain Model ................................................................. 137
6.3.3.2 Examples for Laboratory Test Results Domain Model ...................................................................... 137
6.3.4
Physical Examination — PE .............................................................................................................. 140
6.3.4.1 Assumptions for Physical Examination Domain Model .................................................................... 142
6.3.4.2 Examples for Physical Examination Domain Model ......................................................................... 143
6.3.5
Questionnaire — QS .......................................................................................................................... 144
6.3.5.1 Assumptions for Questionnaire Domain Model ................................................................................ 147
6.3.5.2 Examples for Questionnaire Domain Model ..................................................................................... 148
6.3.6
Subject Characteristics — SC ............................................................................................................ 150
6.3.6.1 Assumptions for Subject Characteristics Domain Model .................................................................. 151
6.3.6.2 Example for Subject Charactistics Domain Model ............................................................................ 152
6.3.7
Vital Signs — VS ............................................................................................................................... 153
6.3.7.1 Assumptions for Vital Signs Domain Model ..................................................................................... 156
6.3.7.2 Example for Vital Signs Domain Model ............................................................................................ 156
6.3.8
Drug Accountability — DA ............................................................................................................... 158
6.3.8.1 Assumptions for Drug Accountability Domain Model ...................................................................... 159
6.3.8.2 Examples for Drug Accountability Domain Model ........................................................................... 160
6.3.9
Microbiology Domains — MB and MS ............................................................................................ 161
6.3.9.1 Microbiology Specimen (MB) Domain Model .................................................................................. 161
6.3.9.2 Assumptions for Microbiology Specimen (MB) Domain Model ...................................................... 164
Microbiology Susceptibility (MS) Domain Model ............................................................................................ 165
6.3.9.3 Assumptions for Microbiology Susceptibility (MS) Domain Model ................................................. 168
6.3.9.4 Examples for MB and MS Domain Models ....................................................................................... 169
6.3.10
Pharmacokinetics Domains — PC and PP ......................................................................................... 172
6.3.10.1 Assumptions for Pharmacokinetic Concentrations (PC) Domain Model........................................... 176
6.3.10.2 Examples for Pharmacokinetic Concentrations (PC) Domain Model ................................................ 176
6.3.10.3 Assumptions for Pharmacokinetic Parameters (PP) Domain Model ................................................. 179
6.3.10.4 Example for Pharmacokinetic Parameters (PP) Domain Model ........................................................ 179
6.3.10.5 Relating PP Records to PC Records .................................................................................................. 181
6.3.10.6 Conclusions........................................................................................................................................ 193
6.3.10.7 Suggestions for Implementing RELREC in the Submission of PK Data ........................................... 193
6.4
FINDINGS ABOUT EVENTS OR INTERVENTIONS ................................................................................................ 194
6.4.1
When to Use Findings About ............................................................................................................. 194
6.4.2
Naming Findings About Domains ..................................................................................................... 195
6.4.3
Variables Unique to Findings About .................................................................................................. 195
6.4.4
Findings About (FA) Domain Model ................................................................................................. 196
6.4.5
Assumptions for Findings About Domain Model .............................................................................. 198
6.4.6
Findings About Examples .................................................................................................................. 199
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CDISC SDTM Implementation Guide (Version 3.1.2)

TRIAL DESIGN DATASETS .............................................................................. 211

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7.1
INTRODUCTION ............................................................................................................................................... 211
7.1.1
Purpose of Trial Design Model .......................................................................................................... 211
7.1.2
Definitions of Trial Design Concepts ................................................................................................ 211
7.1.3
Current and Future Contents of the Trial Design Model .................................................................... 213
7.2
TRIAL ARMS ................................................................................................................................................... 214
7.2.1
Trial Arms Dataset — TA .................................................................................................................. 214
7.2.2
Assumptions for TA Dataset .............................................................................................................. 214
7.2.3
Trial Arms Examples ......................................................................................................................... 215
7.2.3.1 Example Trial 1, a Parallel Trial ........................................................................................................ 216
7.2.3.2 Example Trial 2, a Crossover Trial .................................................................................................... 219
7.2.3.3 Example Trial 3, a Trial with Multiple Branch Points ....................................................................... 223
7.2.3.4 Example Trial 4, Cycles of Chemotherapy ........................................................................................ 226
7.2.3.5 Example Trial 5, Cycles with Different Treatment Durations ............................................................ 230
7.2.3.6 Example Trial 6, Chemotherapy Trial with Cycles of Different Lengths .......................................... 232
7.2.3.7 Example Trial 7, Trial with Disparate Arms ...................................................................................... 235
7.2.4
Issues in Trial Arms Datasets ............................................................................................................. 238
7.2.4.1 Distinguishing between Branches and Transitions ............................................................................ 238
7.2.4.2 Subjects not Assigned to an Arm ....................................................................................................... 238
7.2.4.3 Defining Epochs ................................................................................................................................ 238
7.2.4.4 Rule Variables .................................................................................................................................... 238
7.3
TRIAL ELEMENTS ........................................................................................................................................... 239
7.3.1
Trial Elements Dataset — TE ............................................................................................................ 239
7.3.2
Assumptions for TE Dataset .............................................................................................................. 240
7.3.3
Trial Elements Examples ................................................................................................................... 241
7.3.4
Trial Elements Issues ......................................................................................................................... 242
7.3.4.1 Granularity of Trial Elements ............................................................................................................ 242
7.3.4.2 Distinguishing Elements, Study Cells, and Epochs ........................................................................... 242
7.3.4.3 Transitions between Elements ........................................................................................................... 243
7.4
TRIAL VISITS .................................................................................................................................................. 244
7.4.1
Trial Visits Dataset — TV.................................................................................................................. 244
7.4.2
Assumptions for TV Dataset .............................................................................................................. 244
7.4.3
Trial Visits Examples ......................................................................................................................... 245
7.4.4
Trial Visits Issues ............................................................................................................................... 246
7.4.4.1 Identifying Trial Visits ....................................................................................................................... 246
7.4.4.2 Trial Visit Rules ................................................................................................................................. 246
7.4.4.3 Visit Schedules Expressed with Ranges............................................................................................. 247
7.4.4.4 Contingent Visits................................................................................................................................ 247
7.5
TRIAL INCLUSION/EXCLUSION CRITERIA ........................................................................................................ 248
7.5.1
Trial Inclusion/Exclusion Criteria Dataset — TI ............................................................................... 248
7.5.2
Assumptions for TI Dataset ............................................................................................................... 248
7.5.3
Examples for Trial Inclusion/Exclusion Dataset Model .................................................................... 249
7.6
TRIAL SUMMARY INFORMATION ..................................................................................................................... 249
7.6.1
Trial Summary Dataset — TS ............................................................................................................ 249
7.6.2
Assumptions for Trial Summary Dataset Model ................................................................................ 250
7.6.3
Examples for Trial Summary Dataset Model ..................................................................................... 251
7.7
HOW TO MODEL THE DESIGN OF A CLINICAL TRIAL ....................................................................................... 254
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REPRESENTING RELATIONSHIPS AND DATA .............................................. 255

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8.1
RELATING GROUPS OF RECORDS WITHIN A DOMAIN USING THE --GRPID VARIABLE..................................... 256
8.1.1
--GRPID Example ............................................................................................................................. 256
8.2
RELATING PEER RECORDS .............................................................................................................................. 257
8.2.1
RELREC Dataset ............................................................................................................................... 257
8.2.2
RELREC Dataset Examples .............................................................................................................. 258
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8.3
RELATING DATASETS ...................................................................................................................................... 259
8.3.1
RELREC Dataset Relationship Example ........................................................................................... 259
8.4
RELATING NON-STANDARD VARIABLES VALUES TO A PARENT DOMAIN ......................................................... 260
8.4.1
Supplemental Qualifiers: SUPPQUAL or SUPP-- Datasets .............................................................. 261
8.4.2
Submitting Supplemental Qualifiers in Separate Datasets ................................................................. 262
8.4.3
SUPP-- Examples .............................................................................................................................. 262
8.4.4
When Not to Use Supplemental Qualifiers ........................................................................................ 264
8.5
RELATING COMMENTS TO A PARENT DOMAIN ................................................................................................ 265
8.6
HOW TO DETERMINE WHERE DATA BELONG IN THE SDTM ........................................................................... 265
8.6.1
Guidelines for Determining the General Observation Class .............................................................. 265
8.6.2
Guidelines for Forming New Domains .............................................................................................. 266
8.6.3
Guidelines for Differentiating between Events, Findings, and Findings about Events ...................... 266
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APPENDICES ............................................................................................................. 269
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APPENDIX A: CDISC SDS TEAM *............................................................................................................................. 269
APPENDIX B: GLOSSARY AND ABBREVIATIONS .......................................................................................................... 270
APPENDIX C: CONTROLLED TERMINOLOGY ............................................................................................................... 271
Appendix C1: Controlled Terms or Format for SDTM Variables (see also Appendix C3: Trial Summary Codes 271
Appendix C2: Reserved Domain Codes ................................................................................................................ 274
Appendix C2a: Reserved Domain Codes under Discussion .................................................................................. 277
Appendix C3: Trial Summary Codes ..................................................................................................................... 279
Appendix C4: Drug Accountability Test Codes ..................................................................................................... 283
Appendix C5: Supplemental Qualifiers Name Codes............................................................................................ 283
APPENDIX D: CDISC VARIABLE-NAMING FRAGMENTS ............................................................................................. 284
APPENDIX E: REVISION HISTORY ............................................................................................................................... 286
APPENDIX F: REPRESENTATIONS AND WARRANTIES, LIMITATIONS OF LIABILITY, AND DISCLAIMERS ........................ 298
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1 Introduction
1.1 PURPOSE
This document comprises the CDISC Version 3.1.2 (V3.1.2) Study Data Tabulation Model Implementation Guide
for Human Clinical Trials (SDTMIG), which has been prepared by the Submissions Data Standards (SDS) team of
the Clinical Data Interchange Standards Consortium (CDISC). Like its predecessors, V3.1.2 is intended to guide the
organization, structure, and format of standard clinical trial tabulation datasets submitted to a regulatory authority
such as the US Food and Drug Administration (FDA). V3.1.2 supersedes all prior versions of the CDISC
Submission Data Standards.
The SDTMIG should be used in close concert with the current version of the CDISC Study Data Tabulation Model
(SDTM, available at http://www.cdisc.org/standards) that describes the general conceptual model for representing
clinical study data that is submitted to regulatory authorities and should be read prior to reading the SDTMIG.
V3.1.2 provides specific domain models, assumptions, business rules, and examples for preparing standard
tabulation datasets that are based on the SDTM.
Tabulation datasets, which are electronic listings of individual observations for a subject that comprise the essential
data reported from a clinical trial, are one of four types of data currently submitted to the FDA along with patient
profiles, listings, and analysis files. By submitting tabulations that conform to the standard structure, sponsors may
benefit by no longer having to submit separate patient profiles or listings with a product marketing application.
SDTM datasets are not intended to fully meet the needs supported by analysis datasets, which will continue to be
submitted separately in addition to the tabulations. Since July 2004, the FDA has referenced use of the SDTM in the
Study Data Specifications for the Electronic Common Technical Document, available at
http://www.fda.gov/cder/regulatory/ersr/Studydata-v1.2.pdf.
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The availability of standard submission data will provide many benefits to regulatory reviewers. Reviewers can be
trained in the principles of standardized datasets and the use of standard software tools, and thus be able to work
with the data more effectively with less preparation time. Another benefit of the standardized datasets is that they
will support 1) the FDA‘s efforts to develop a repository for all submitted trial data, and 2) a suite of standard review
tools to access, manipulate, and view the tabulations. Use of these data standards is also expected to benefit industry
by streamlining the flow of data from collection through submission, and facilitating data interchange between
partners and providers. Note that the SDTM represents an interchange standard, rather than a presentation format. It
is assumed that tabulation data will be transformed by software tools to better support viewing and analysis.
This document is intended for companies and individuals involved in the collection, preparation, and analysis of
clinical data that will be submitted to regulatory authorities.

1.2 ORGANIZATION OF THIS DOCUMENT
This document is organized into the following sections:



Section 1, Introduction, provides an overall introduction to the V3.1.2 models and describes changes from
prior versions.
Section 2, Fundamentals of the SDTM, recaps the basic concepts of the SDTM, and describes how this
implementation guide should be used in concert with the SDTM.
Section 3, Submitting Data in Standard Format, explains how to describe metadata for regulatory
submissions, and how to assess conformance with the standards.
Section 4, Assumptions for Domain Models, describes basic concepts, business rules, and assumptions that
should be taken into consideration before applying the domain models.


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




Section 5, Models for Special-Purpose Domains, describes special-purpose domains, including
Demographics, Comments, Subject Visits, and Subject Elements.
Section 6, Domain Models Based on the General Observation Classes, provides specific metadata models
based on the three general observation classes, along with assumptions and example data.
Section 7, Trial Design Datasets, provides specific metadata models, assumptions, and examples.
Section 8, Representing Relationships and Data, describes how to represent relationships between separate
domains, datasets, and/or records, and information to help sponsors determine where data belongs in the
SDTM.
Appendices provide additional background material and describe other supplemental material relevant to
implementation.
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1.3 RELATIONSHIP TO PRIOR CDISC DOCUMENTS
This document, together with the SDTM, represents the most recent version of the CDISC Submission Data Domain
Models. Since all updates are intended to be backward compatible the term ―V3.x‖ is used to refer to Version 3.1
and all subsequent versions. The most significant changes since the prior version, V3.1.1, include:



New domain models for Clinical Events and Findings About Events and Interventions (formerly Clinical
Findings in v3.1.2 Draft), and inclusion of previously posted domain models for Protocol Deviations, Drug
Accountability, pharmacokinetic data, and microbiology.
Additional assumptions and rules for representing common data scenarios and naming of datasets in
Section 4, including guidance on the use of keys and representing data with multiple values for a single
question.
Corrections and clarifications regarding the use of ISO 8601 date formats in Section 4.1.4.
Additional guidance about how to address Findings data collected as a result of Events or Interventions,
and data submitted for pre-specified Findings and Events.
The use of new SDTM variables (Section 6.2 of the SDTM).
Implementation advice on the use of new timing variables, --STRTPT, --ENRTPT, --STTPT, and --ENTPT
(Section 4.1.4.7), and the new variable --OBJ (Section 6.4.3).
Listing of Qualifier variables from the same general observation class that would not generally be used in
the standard domains.
Several changes to the organization of the document, including the reclassification of Subject Elements
(SE) and Subject Visits (SV) as special-purpose domain datasets in Section 5 (these were formerly included
as part of Trial Design), and moving data examples from a separate section (former Section 9) to locations
immediately following each domain model in Section 5 and Section 6.
Changes to the method for representing multiple RACE values in DM and SUPPDM with examples.
Removed the Origin column from domain models based on the three general classes since origins will need
to be defined by the sponsor in most cases. Definitions of origin metadata have been added.
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
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A detailed list of changes between versions is provided in Appendix E.
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V3.1 was the first fully implementation-ready version of the CDISC Submission Data Standards that was directly
referenced by the FDA for use in human clinical studies involving drug products. However, future improvements
and enhancements such as V3.1.2 will continue to be made as sponsors gain more experience submitting data in this
format. Therefore, CDISC will be preparing regular updates to the implementation guide to provide corrections,
clarifications, additional domain models, examples, business rules, and conventions for using the standard domain
models. CDISC will produce further documentation for controlled terminology as separate publications, so sponsors
are encouraged to check the CDISC website ( www.cdisc.org/standards/) frequently for additional information. See
Section 4.1.3 for the most up-to-date information on applying Controlled Terminology.
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1.4 HOW TO READ THIS IMPLEMENTATION GUIDE
This SDTM Implementation Guide (SDTMIG) is best read online, so the reader can benefit from the many
hyperlinks included to both internal and external references. The following guidelines may be helpful in reading this
document:
1.
2.

First, read the SDTM to gain a general understanding of SDTM concepts.
Next, read Sections 1-3 of this document to review the key concepts for preparing domains and submitting
data to regulatory authorities. Refer to the Glossary in Appendix B as necessary.
Read the General Assumptions for all Domains in Section 4.
Review Section 5 and Section 6 in detail, referring back to Assumptions as directed (hyperlinks are
provided). Note the implementation examples for each domain to gain an understanding of how to apply
the domain models for specific types of data.
Read Section 7 to understand the fundamentals of the Trial Design Model and consider how to apply the
concepts for typical protocols. New extensions to the trial design model will be published separately on the
CDISC website.
Review Section 8 to learn advanced concepts of how to express relationships between datasets, records and
additional variables not specifically defined in the models.
Finally, review the Appendices as appropriate.
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3.
4.

6.
7.

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1.5 SUBMITTING COMMENTS
Comments on this document can be submitted through the CDISC Discussion Board.
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2 Fundamentals of the SDTM
2.1 OBSERVATIONS AND VARIABLES
The V3.x Submission Data Standards are based on the SDTM‘s general framework for organizing clinical trials
information that is to be submitted to the FDA. The SDTM is built around the concept of observations collected
about subjects who participated in a clinical study. Each observation can be described by a series of variables,
corresponding to a row in a dataset or table. Each variable can be classified according to its Role. A Role determines
the type of information conveyed by the variable about each distinct observation and how it can be used. Variables
can be classified into five major roles:


Identifier variables, such as those that identify the study, subject, domain, and sequence number of the record



Topic variables, which specify the focus of the observation (such as the name of a lab test)



Timing variables, which describe the timing of the observation (such as start date and end date)



Qualifier variables, which include additional illustrative text or numeric values that describe the results or
additional traits of the observation (such as units or descriptive adjectives)



Rule variables, which express an algorithm or executable method to define start, end, and branching or looping
conditions in the Trial Design model

The set of Qualifier variables can be further categorized into five sub-classes:


Grouping Qualifiers are used to group together a collection of observations within the same domain. Examples
include --CAT and --SCAT.



Result Qualifiers describe the specific results associated with the topic variable in a Findings dataset. They
answer the question raised by the topic variable. Result Qualifiers are --ORRES, --STRESC, and --STRESN.



Synonym Qualifiers specify an alternative name for a particular variable in an observation. Examples include
--MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable, --TEST and
--LOINC which are equivalent terms for a --TESTCD.



Record Qualifiers define additional attributes of the observation record as a whole (rather than describing a
particular variable within a record). Examples include --REASND, AESLIFE, and all other SAE flag variables
in the AE domain; AGE, SEX, and RACE in the DM domain; and --BLFL, --POS, --LOC, --SPEC and --NAM
in a Findings domain



Variable Qualifiers are used to further modify or describe a specific variable within an observation and are only
meaningful in the context of the variable they qualify. Examples include --ORRESU, --ORNRHI, and
--ORNRLO, all of which are Variable Qualifiers of --ORRES; and --DOSU, which is a Variable Qualifier of
--DOSE.

For example, in the observation, ―Subject 101 had mild nausea starting on Study Day 6, ― the Topic variable value is
the term for the adverse event, ―NAUSEA‖. The Identifier variable is the subject identifier, ―101‖. The Timing
variable is the study day of the start of the event, which captures the information, ―starting on Study Day 6‖, while
an example of a Record Qualifier is the severity, the value for which is ―MILD‖. Additional Timing and Qualifier
variables could be included to provide the necessary detail to adequately describe an observation.

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2.2 DATASETS AND DOMAINS
Observations about study subjects are normally collected for all subjects in a series of domains. A domain is defined
as a collection of logically related observations with a common topic. The logic of the relationship may pertain to the
scientific subject matter of the data or to its role in the trial. Each domain is represented by a single dataset.
Each domain dataset is distinguished by a unique, two-character code that should be used consistently throughout
the submission. This code, which is stored in the SDTM variable named DOMAIN, is used in four ways: as the
dataset name, the value of the DOMAIN variable in that dataset, as a prefix for most variable names in that dataset,
and as a value in the RDOMAIN variable in relationship tables ( Section 8).
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All datasets are structured as flat files with rows representing observations and columns representing variables. Each
dataset is described by metadata definitions that provide information about the variables used in the dataset. The
metadata are described in a data definition document named ―define‖ that is submitted with the data to regulatory
authorities. (See the Case Report Tabulation Data Definition Specification [define.xml], available at www.CDISC.org).
Define.xml specifies seven distinct metadata attributes to describe SDTM data:
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

The Variable Name (limited to 8 characters for compatibility with the SAS Transport format)



A descriptive Variable Label, using up to 40 characters, which should be unique for each variable in the dataset



The data Type (e.g., whether the variable value is a character or numeric)



The set of controlled terminology for the value or the presentation format of the variable (Controlled Terms or Format)



The Origin of each variable (see Section 4.1.1.8)



The Role of the variable, which determines how the variable is used in the dataset. For the V3.x domain models,
Roles are used to represent the categories of variables such as Identifier, Topic, Timing, or the five types of
Qualifiers.



Comments or other relevant information about the variable or its data included by the sponsor as necessary to
communicate information about the variable or its contents to a regulatory agency.

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Data stored in SDTM datasets include both raw (as originally collected) and derived values (e.g., converted into
standard units, or computed on the basis of multiple values, such as an average). The SDTM lists only the name,
label, and type, with a set of brief CDISC guidelines that provide a general description for each variable used for a
general observation class.
The domain dataset models included in Section 5 and Section 6 of this document provide additional information
about Controlled Terms or Format, notes on proper usage, and examples. Controlled terminology (CT) is now
represented one of four ways:
 A single asterisk when there is no specific CT available at the current time, but the SDS Team expects that sponsors
may have their own CT and/or the CDISC Controlled Terminology Team may be developing CT.
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A list of controlled terms for the variable when values are not yet maintained externally



The name of an external codelist whose values can be found via the hyperlinks in either the domain or Appendix C1.



A common format such as ISO 8601

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The CDISC Controlled Terminology team will be publishing additional guidance on use of controlled terminology
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2.3 SPECIAL-PURPOSE DATASETS
The SDTM includes three types of special-purpose datasets:


Domain datasets, consisting of Demographics (DM), Comments (CO), Subject Elements (SE), and Subject
Visits (SV) 1, all of which include subject-level data that do not conform to one of the three general
observation classes. These are described in Section 5.
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Trial Design Model (TDM) datasets, such as Trial Arms (TA) and Trial Elements (TE), which represent
information about the study design but do not contain subject data. These are described in Section 7.
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

Relationship datasets, which include the RELREC and SUPP-- datasets described in Section 8.
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2.4 THE GENERAL OBSERVATION CLASSES
Most subject-level observations collected during the study should be represented according to one of the three
SDTM general observation classes: Interventions, Events, or Findings. The lists of variables allowed to be used in
each of these can be found in the STDM.


The Interventions class captures investigational, therapeutic and other treatments that are administered to the
subject (with some actual or expected physiological effect) either as specified by the study protocol (e.g.,
exposure to study drug), coincident with the study assessment period (e.g., concomitant medications), or
self-administered by the subject (such as use of alcohol, tobacco, or caffeine).



The Events class captures planned protocol milestones such as randomization and study completion, and
occurrences, conditions, or incidents independent of planned study evaluations occurring during the trial (e.g.,
adverse events) or prior to the trial (e.g., medical history).



The Findings class captures the observations resulting from planned evaluations to address specific tests or
questions such as laboratory tests, ECG testing, and questions listed on questionnaires.

In most cases, the choice of observation class appropriate to a specific collection of data can be easily determined
according to the descriptions provided above. The majority of data, which typically consists of measurements or
responses to questions usually at specific visits or time points, will fit the Findings general observation class.
Additional guidance on choosing the appropriate general observation class is provided in Section 8.6.1.
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General assumptions for use with all domain models and custom domains based on the general observation classes
are described in Section 4 of this document; specific assumptions for individual domains are included with the
domain models.
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2.5 THE SDTM STANDARD DOMAIN MODELS
The following standard domains with their respective domain codes have been defined or referenced by the CDISC SDS
Team in this document. Note that other domain models may be posted separately for comment after this publication.
Special-Purpose Domains (defined in Section 5):
 Demographics — DM

 Subject Elements — SE

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Comments — CO
Subject Visits — SV
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Interventions General Observation Class (defined in Section 6.1):
 Concomitant Medications — CM
 Exposure — EX
 Substance Use — SU
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Events General Observation Class (defined in Section 6.2):
 Adverse Events — AE
 Disposition — DS
 Medical History — MH
 Protocol Deviations — DV
 Clinical Events — CE
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Findings General Observation Class (defined in Section 6.3):
 ECG Test Results — EG
 Inclusion/Exclusion Criterion Not Met — IE
 Laboratory Test Results — LB
 Physical Examination — PE
 Questionnaires — QS
 Subject Characteristics — SC
 Vital Signs — VS
 Drug Accountability — DA
 Microbiology Specimen — MB
 Microbiology Susceptibility Test — MS
 PK Concentrations — PC
 PK Parameters —PP
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Findings About (defined in Section 6.4)
 Findings About — FA
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Trial Design Domains (defined in Section 7):
 Trial Arms — TA
 Trial Visits — TV
 Trial Summary — TS
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

Trial Elements — TE
Trial Inclusion/Exclusion Criteria — TI
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Relationship Datasets (defined in Section 8):
 Supplemental Qualifiers — SUPPQUAL or
multiple SUPP-- datasets
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

Related Records — RELREC
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A sponsor should only submit domain datasets that were actually collected (or directly derived from the collected
data) for a given study. Decisions on what data to collect should be based on the scientific objectives of the study,
rather than the SDTM. Note that any data that was collected and will be submitted in an analysis dataset must also
appear in a tabulation dataset.
The collected data for a given study may use some or all of the SDS standard domains as well as additional custom
domains based on the three general observation classes. A list of standard domain codes for many commonly used
domains is provided in . Additional standard domain models will be published by CDISC as they are developed, and
sponsors are encouraged to check the CDISC website for updates.
These general rules apply when determining which variables to include in a domain:




The Identifier variables, STUDYID, USUBJID, DOMAIN, and --SEQ are required in all domains based on the
general observation classes. Other Identifiers may be added as needed.
Any Timing variables are permissible for use in any submission dataset based on a general observation class
except where restricted by specific domain assumptions.
Any additional Qualifier variables from the same general observation class may be added to a domain model
except where restricted by specific domain assumptions.

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

Sponsors may not add any other variables than those described in the preceding three bullets. The addition of
non-standard variables will compromise the FDA‘s abilities to populate the data repository and to use standard
tools. The SDTM allows for the inclusion of the sponsors non-SDTM variables using the Supplemental
Qualifiers special-purpose dataset structure, described in Section 8.4. As the SDTM continues to evolve over
time, certain additional standard variables may be added to the general observation classes. Therefore, Sponsors
wishing to nominate such variables for future consideration should provide a rationale and description of the
proposed variable(s) along with representative examples to the CDISC Public Discussion Forum.
Standard variables must not be renamed or modified for novel usage. Their metadata should not be changed.
As long as no data was collected for Permissible variables, a sponsor is free to drop them and the corresponding
descriptions from the define.xml.
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2.6 CREATING A NEW DOMAIN
This section describes the overall process for creating a custom domain, which must be based on one of the three
SDTM general observation classes. The number of domains submitted should be based on the specific requirements
of the study. Follow the process below to create a custom domain:
1.

Confirm that none of the existing published domains will fit the need. A custom domain may only be
created if the data are different in nature and do not fit into an existing published domain.
 Establish a domain of a common topic (i.e., where the nature of the data is the same), rather than by
a specific method of collection (e.g. electrocardiogram - EG). Group and separate data within the
domain using --CAT, --SCAT, --METHOD, --SPEC, --LOC, etc. as appropriate. Examples of
different topics are: microbiology, tumor measurements, pathology/histology, vital signs, and
physical exam results.
 Do not create separate domains based on time, rather represent both prior and current observations
in a domain (e.g., CM for all non-study medications). Note that AE and MH are an exception to this
best practice because of regulatory reporting needs.
 How collected data are used (e.g., to support analyses and/or efficacy endpoints) must not result in
the creation of a custom domain. For example, if blood pressure measurements are endpoints in a
hypertension study, they must still be represented in the VS (Vital Signs) domain as opposed to a
custom ―efficacy‖ domain. Similarly, if liver function test results are of special interest, they must
still be represented in the LB (Laboratory Tests) domain.
 Data that were collected on separate CRF modules or pages may fit into an existing domain (such as
separate questionnaires into the QS domain, or prior and concomitant medications in the CM domain).
 If it is necessary to represent relationships between data that are hierarchical in nature (e.g., a parent
record must be observed before child records), then establish a domain pair (e.g., MB/MS, PC/PP).
Note, domain pairs have been modeled for microbiology data (MB/MS domains) and PK data
(PC/PP domains) to enable dataset-level relationships to be described using RELREC. The domain
pair uses DOMAIN as an Identifier to group parent records (e.g., MB) from child records (e.g., MS)
and enables a dataset-level relationship to be described in RELREC. Without using DOMAIN to
facilitate description of the data relationships, RELREC, as currently defined could not be used
without introducing a variable that would group data like DOMAIN.

Check the Submission Data Standards area of the CDISC website ( http://www.cdisc.org/) for models added
after the last publication of the SDTMIG.

Look for an existing, relevant domain model to serve as a prototype. If no existing model seems
appropriate, choose the general observation class (Interventions, Events, or Findings) that best fits the data
by considering the topic of the observation The general approach for selecting variables for a custom
domain is as follows (also see Figure 2.6 below)
a. Select and include the required Identifier variables (e.g., STUDYID, DOMAIN, USUBJID, --SEQ)
and any permissible Identifier variables from SDTM Table 2.2.4.
b. Include the Topic variable from the identified general observation class (e.g., --TESTCD for
Findings) (SDTM table 2.2.1, SDTM table 2.2.2 or SDTM table 2.2.3).
c. Select and include the relevant Qualifier variables from the identified general observation class
(SDTM table 2.2.1, SDTM table 2.2.2 or SDTM table 2.2.3). Variables belonging to other general
observation classes must not be added.

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d.

Select and include the applicable Timing variables (SDTM Table 2.2.5). Determine the domain
code. Check Appendix C2 and Appendix C2A for reserved two-character domain identifiers or
abbreviations. If one has not been assigned by CDISC, then the sponsor may select the unique
two-character domain code to be used consistently throughout the submission.
Apply the two-character domain code to the appropriate variables in the domain. Replace all
variable prefixes (shown in the models as two hyphens ―--―) with the domain code. If no domain
code exists in Appendix C2 or Appendix C2A for this data and if it desired to have this domain code
as part of CDISC controlled terminology then submit a request to add the new domain via the
CDISC website. Requests for new domain codes must include:
1) Two-letter domain code and description
2) Rationale for domain code
3) Domain model with assumptions
4) Examples
Upon receipt, the SDS Domain Code Subteam will review the package. If accepted, then the
proposal will be submitted to the SDS Team for review. Upon approval, a response will be sent to
the requestor and package processing will begin (i.e., prepare for inclusion in a next release of the
SDTM and SDTMIG, mapping concepts to BRIDG, and posting an update to the CDISC website). If
declined, then the Domain Code Subteam will draft a response for SDS Team review. Upon
agreement, the response will be sent to the requestor and also posted to the CDISC website.
Set the order of variables consistent with the order defined in SDTM Tables 2.2.1, 2.2.2, or 2.2.3,
depending upon the general observation class the custom domain is based on.
Adjust the labels of the variables only as appropriate to properly convey the meaning in the context
of the data being submitted in the newly created domain. Use title case for all labels (title case
means to capitalize the first letter of every word except for articles, prepositions, and conjunctions).
Ensure that appropriate standard variables are being properly applied by comparing the use of
variables in standard domains.
Describe the dataset within the define.xml document (see Section 3.2).
Place any non-standard (SDTM) variables in a Supplemental Qualifier dataset. Mechanisms for
representing additional non-standard Qualifier variables not described in the general observation
classes and for defining relationships between separate datasets or records are described in Section 8.4
of this document.
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g.

h.
i.
j.

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Figure 2.6. Creating a New Domain

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3 Submitting Data in
Standard Format
3.1 STANDARD METADATA FOR DATASET CONTENTS AND ATTRIBUTES
The SDTMIG provides standard descriptions of some of the most commonly used data domains, with metadata
attributes. The descriptive metadata attributes that should be included in a define.xml as applied in the domain
models are:


The SDTMIG -standard variable name (standardized for all submissions, even though sponsors may be
using other variable names internally in their operational database)



The SDTMIG -standard variable label



Expected data types (the SDTMIG uses character or numeric to conform to the data types consistent with
SAS V5 transport file format, but define.xml allows for more descriptive data types, such as integer or
float)



The actual controlled terms and formats used by the sponsor (do not include the asterisk (*) included in the
CDISC domain models to indicate when controlled terminology applies)



The origin or source of the data (e.g., CRF, derived; see definitions in Section 4.1.1.8)



The role of the variable in the dataset corresponding to the role in the SDTM if desired. Since these roles
are predefined for all standard domains that follow the general observation classes, they do not need to be
specified by sponsors in their define.xml for these domains.



Any Comments provided by the sponsor that may be useful to the Reviewer in understanding the variable
or the data in it.

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In addition to these metadata attributes, the CDISC domain models include three other shaded columns that are not
sent to the FDA. These columns assist sponsors in preparing their datasets:
 "CDISC Notes" is for notes to the sponsor regarding the relevant to the use of each variable
 "Core" indicates how a variable is classified as a CDISC Core Variable (see Section 4.1.1.5)
 "References" provides references to relevant section of the SDTM or the SDTMIG.), and one to provide
references to relevant section of the SDTM or the SDTMIG.
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The domain models in Section 6 illustrate how to apply the SDTM when creating a specific domain dataset. In
particular, these models illustrate the selection of a subset of the variables offered in one of the general observation
classes along with applicable timing variables. The models also show how a standard variable from a general
observation class should be adjusted to meet the specific content needs of a particular domain, including making the
label more meaningful, specifying controlled terminology, and creating domain-specific notes and examples. Thus
the domain models demonstrate not only how to apply the model for the most common domains, but also give
insight on how to apply general model concepts to other domains not yet defined by CDISC.
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3.2 USING THE CDISC DOMAIN MODELS IN REGULATORY
SUBMISSIONS — DATASET METADATA
The define.xml that accompanies a submission should also describe each dataset that is included in the submission
and describe the natural key structure of each dataset. While most studies will include DM and a set of safety
domains based on the three general observation classes (typically including EX, CM, AE, DS, MH, IE, LB, and VS),
the actual choice of which data to submit will depend on the protocol and the needs of the regulatory reviewer.
Dataset definition metadata should include dataset filenames, descriptions, locations, structures, class, purpose, keys,
and comments as described below in Table 3.2.1.
In the event that no records are present in a dataset (e.g., a small PK study where no subjects took concomitant
medications), the empty dataset should not be submitted and should not be described in the define.xml document.
The annotated CRF will show the data that would have been submitted had data been received; it need not be reannotated to indicate that no records exist.

Table 3.2.1. SDTM Submission Dataset-Definition Metadata Example
Dataset

Description

Demographics

Comments

Class

Structure

Purpose

Special Purpose
Domains
Special Purpose
Domains

One record per subject

Tabulation

One record per comment
per subject

Tabulation

Subject Elements

Special Purpose
Domains

One record per actual
Element per subject

Tabulation

Subject Visits

Special Purpose
Domains

One record per actual visit
per subject

Tabulation

Concomitant
Medications

Interventions

Tabulation

Exposure

Interventions

One record per recorded
medication occurrence or
constant-dosing interval per
subject.
One record per constant
dosing interval per subject

Substance Use

Interventions

One record per substance
type per reported occurrence
per subject

Tabulation

Adverse Events

Events

One record per adverse
event per subject

Tabulation

Disposition

Events

One record per disposition
status or protocol milestone
per subject

Tabulation

Medical History

Events

One record per medical
history event per subject

Tabulation

Protocol
Deviations

Events

One record per protocol
deviation per subject

Tabulation

Clinical Events

Events

One record per event per
subject

Tabulation

1839H

1840H

184H

1842H

1843H

184H

1845H

1846H

1847H

184H

1850H

1849H

Tabulation

Keys*
183H

STUDYID,
USUBJID
STUDYID,
USUBJID,
COSEQ
STUDYID,
USUBJID,
ETCD,
SESTDTC
STUDYID,
USUBJID,
VISITNUM
STUDYID,
USUBJID,
CMTRT,
CMSTDTC
STUDYID,
USUBJID,
EXTRT,
EXSTDTC
STUDYID,
USUBJID,
SUTRT,
SUSTDTC
STUDYID,
USUBJID,
AEDECOD,
AESTDTC
STUDYID,
USUBJID,
DSDECOD,
DSSTDTC
STUDYID,
USUBJID,
MHDECOD
STUDYID,
USUBJID,
DVTERM,
DVSTDTC
STUDYID,
USUBJID,
CETERM,
CESTDTC

Location
dm.xpt
co.xpt

se.xpt

sv.xpt

cm.xpt

ex.xpt

su.xpt

ae.xpt

ds.xpt

mh.xpt

dv.xpt

ce.xpt

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CDISC SDTM Implementation Guide (Version 3.1.2)

Dataset

Description

Class

Structure

Purpose

ECG Test Results

Findings

One record per ECG
observation per time point
per visit per subject

Tabulation

Inclusion/
Exclusion Criteria
Not Met
Laboratory Tests
Results

Findings

One record per
inclusion/exclusion criterion
not met per subject
One record per analyte per
planned time point number
per time point reference per
visit per subject

Tabulation

Physical
Examination

Findings

One record per body system
or abnormality per visit per
subject

Tabulation

Questionnaires

Findings

One record per
questionnaire per question
per time point per visit per
subject

Tabulation

Subject
Characteristics

Findings

One record per
characteristic per subject

Tabulation

Vital Signs

Findings

One record per vital sign
measurement per time point
per visit per subject

Tabulation

Drug
Accountability

Findings

One record per drug
accountability finding per
subject

Tabulation

Microbiology
Specimen

Findings

One record per
microbiology specimen
finding per time point per
visit per subject

Tabulation

Microbiology
Susceptibility Test

Findings

One record per
microbiology susceptibility
test (or other organismrelated finding) per
organism found in MB

Tabulation

Pharmacokinetic
Concentrations

Findings

One record per analyte per
planned time point number
per time point reference per
visit per subject"

Tabulation

185H

1852H

LB
1853H

1854H

185H

1856H

1857H

185H

1859H

186H

1860H

Page 18
November 12, 2008

Findings

Tabulation

Keys*
183H

STUDYID,
USUBJID,
EGTESTCD,
VISITNUM,
EGTPTREF,
EGTPTNUM
STUDYID,
USUBJID,
IETESTCD
STUDYID,
USUBJID,
LBTESTCD,
LBSPEC,
VISITNUM,
LBTPTREF,
LBTPTNUM
STUDYID,
USUBJID,
PETESTCD,
VISITNUM
STUDYID,
USUBJID,
QSCAT,
QSTESTCD,
VISITNUM,
QSTPTREF,
QSTPTNUM
STUDYID,
USUBJID,
SCTESTCD
STUDYID,
USUBJID,
VSTESTCD,
VISITNUM,
VSTPTREF,
VSTPTNUM
STUDYID,
USUBJID,
DATESTCD,
DADTC
STUDYID,
USUBJID,
MBTESTCD,
VISITNUM,
MBTPTREF,
MBTPTNUM
STUDYID,
USUBJID,
MSTESTCD,
VISITNUM,
MSTPTREF,
MSTPTNUM
STUDYID,
USUBJID,
PCTESTCD,
VISITNUM,
PCTPTREF,
PCTPTNUM

Location
eg.xpt

ie.xpt

lb.xpt

pe.xpt

qs.xpt

sc.xpt

vs.xpt

da.xpt

mb.xpt

ms.xpt

pc.xpt

CDISC SDTM Implementation Guide (Version 3.1.2)
Dataset

Description

Class

Structure

Purpose

Pharmacokinetic
Parameters

Findings

One record per PK
parameter per timeconcentration profile per
modeling method per
subject

Tabulation

Findings About
Events or
Interventions

Findings

One record per finding per
object per time point per
time point reference per
visit per subject

Tabulation

Trial Arms

Trial Design

One record per planned
Element per Arm

Tabulation

Trial Elements

Trial Design

Tabulation

Trial Visits

Trial Design

One record per planned
Element
One record per planned
Visit per Arm

Trial Design

One record per I/E criterion

Tabulation

Trial Inclusion/
Exclusion Criteria
Trial Summary

Trial Design

One record per trial
summary parameter
value

Tabulation

RELREC

Related Records

Special Purpose
Datasets

One record per related
record, group of records or
datasets

Tabulation

SUPP-**

Supplemental
Qualifiers for
[domain name]

Special-Purpose
Datasets

One record per IDVAR,
IDVARVAL, and QNAM
value per subject

Tabulation

302H

30H

304H

305H

1862H

306H

307H

1863H

308H

Tabulation

Keys*
183H

Location

STUDYID,
USUBJID,
PPTESTCD,
PPCAT,
VISITNUM,
PPTPTREF
STUDYID,
USUBJID,
FATESTCD,
FAOBJ,
VISITNUM,
FATPTREF,
FATPTNUM
STUDYID,
ARMCD,
TAETORD
STUDYID,
ETCD
STUDYID,
VISITNUM,
ARMCD
STUDYID,
IETESTCD
STUDYID,
TSPARMCD,
TSSEQ

pp.xpt

STUDYID,
RDOMAIN,
USUBJID,
IDVAR,
IDVARVAL,
RELID
STUDYID,
RDOMAIN,
USUBJID,
IDVAR,
IDVARVAL,
QNAM

relrec.xpt

fa.xpt

ta.xpt

te.xpt
tv.xpt

ti.xpt
ts.xpt

supp--.xpt or
suppqual.xpt

Note that the key variables shown in this table are examples only. A sponsor‘s actual key structure may be
different.
** Separate Supplemental Qualifier datasets of the form supp--.xpt are recommended. See Section 8.4.
*

309H

3.2.1.1 PRIMARY KEYS
Table 3.2.1 above shows examples of what a sponsor might submit as variables that comprise the primary key for
SDTM datasets. Since the purpose of this column is to aid reviewers in understanding the structure of a dataset,
sponsors should list all of the natural keys (see definition below) for the dataset. These keys should define uniqueness
for records within a dataset, and may define a record sort order. The naming of these keys should be consistent with
the description of the structure in the Structure column. For all the general-observation-class domains (and for some
special-purpose domains), the --SEQ variable was created so that a unique record could be identified consistently
across all of these domains via its use, along with STUDYID, USUBJID, DOMAIN. In most domains, --SEQ will be
a surrogate key (see definition below) for a set of variables which comprise the natural key. In certain instances, a
Supplemental Qualifier (SUPP--) variable might also contribute to the natural key of a record for a particular domain.
See assumption 4.1.1.9 for how this should be represented, and for additional information on keys.
310H

31H

A natural key is a piece of data (one or more columns of an entity) that uniquely identify that entity, and distinguish
it from any other row in the table. The advantage of natural keys is that they exist already, and one does not need to
introduce a new ―unnatural‖ value to the data schema. One of the difficulties in choosing a natural key is that just
about any natural key one can think of has the potential to change. Because they have business meaning, natural
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Final

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CDISC SDTM Implementation Guide (Version 3.1.2)

keys are effectively coupled to the business, and they may need to be reworked when business requirements change.
An example of such a change in clinical trials data would be the addition of a position or location that becomes a
key in a new study, but wasn‘t collected in previous studies.
312H

A surrogate key is a single-part, artificially established identifier for a record. Surrogate key assignment is a special
case of derived data, one where a portion of the primary key is derived. A surrogate key is immune to changes in
business needs. In addition, the key depends on only one field, so it‘s compact. A common way of deriving surrogate
key values is to assign integer values sequentially. The --SEQ variable in the SDTM datasets is an example of a
surrogate key for most datasets; in some instances, however, --SEQ might be a part of a natural key as a replacement
for what might have been a key (e.g. a repeat sequence number) in the sponsor's database
3.2.1.2 CDISC SUBMISSION VALUE-LEVEL METADATA
In general, the CDISC V3.x Findings data models are closely related to normalized, relational data models in a
vertical structure of one record per observation. Since the V3.x data structures are fixed, sometimes information that
might have appeared as columns in a more horizontal (denormalized) structure in presentations and reports will
instead be represented as rows in an SDTM Findings structure. Because many different types of observations are all
presented in the same structure, there is a need to provide additional metadata to describe the expected differences
that differentiate, for example, hematology lab results from serum chemistry lab results in terms of data type,
standard units and other attributes.
For example, the Vital Signs data domain could contain subject records related to diastolic and systolic blood
pressure, height, weight, and body mass index (BMI). These data are all submitted in the normalized SDTM
Findings structure of one row per vital signs measurement. This means that there could be five records per subject
(one for each test or measurement) for a single visit or time point, with the parameter names stored in the Test
Code/Name variables, and the parameter values stored in result variables. Since the unique Test Code/Names could
have different attributes (i.e., different origins, roles, and definitions) there would be a need to provide value-level
metadata for this information.
The value-level metadata should be provided as a separate section of the Case Report Tabulation Data Definition
Specification (CRT-DDS). This information, which historically has been submitted as a pdf document named
―define.pdf‖, should henceforth be submitted in an XML format. For details on the CDISC specification for
submitting define.xml, see www.cdisc.org/standards/
31H

3.2.2 CONFORMANCE
Conformance with the SDTMIG Domain Models is minimally indicated by:


Following the complete metadata structure for data domains



Following SDTMIG domain models wherever applicable



Using SDTM-specified standard domain names and prefixes where applicable



Using SDTM-specified standard variable names



Using SDTM-specified variable labels for all standard domains



Using SDTM-specified data types for all variables



Following SDTM-specified controlled terminology and format guidelines for variables, when provided



Including all collected and relevant derived data in one of the standard domains, special-purpose datasets, or
general-observation-class structures



Including all Required and Expected variables as columns in standard domains, and ensuring that all
Required variables are populated



Ensuring that each record in a dataset includes the appropriate Identifier and, Timing variables, as well as a
Topic variable



Conforming to all business rules described in the CDISC Notes column and general and domain-specific
assumptions.

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CDISC SDTM Implementation Guide (Version 3.1.2)

4 Assumptions for Domain
Models
4.1 GENERAL ASSUMPTIONS FOR ALL DOMAINS
4.1.1 GENERAL DOMAIN ASSUMPTIONS
4.1.1.1 REVIEW STUDY DATA TABULATION AND IMPLEMENTATION GUIDE
Review the Study Data Tabulation Model as well as this Implementation Guide before attempting to use any of the
individual domain models. See the Case Report Tabulation Data Definition Specification (define.xml), available on
the CDISC website, for information about an xml representation of the define.xml document.
4.1.1.2 RELATIONSHIP TO ANALYSIS DATASETS
Specific guidance on preparing analysis datasets can be found in the CDISC Analysis Dataset Model General
Considerations document, available at www.cdisc.org/standards/
314H

4.1.1.3 ADDITIONAL TIMING VARIABLES
Additional Timing variables can be added as needed to a standard domain model based on the three general
observation classes except where discouraged in Assumption 4.1.4.8 and specific domain assumptions. Timing
variables can be added to special-purpose domains only where specified in the SDTMIG domain model
assumptions. Timing variables cannot be added to SUPPQUAL datasets or to RELREC (described in Section 8).
315H

316H

4.1.1.4 ORDER OF THE VARIABLES
The order of variables in the define.xml should reflect the order of variables in the dataset. The order of variables in
the CDISC domain models has been chosen to facilitate the review of the models and application of the models.
Variables for the three general observation classes should be ordered with Identifiers first, followed by the Topic,
Qualifier, and Timing variables. Within each role, variables are ordered as shown in Tables 2.2.1, 2.2.2, 2.2.3,
2.2.3.1, 2.2.4, and 2.2.5 of the SDTM.
4.1.1.5 CDISC CORE VARIABLES
The concept of core variable is used both as a measure of compliance, and to provide general guidance to sponsors.
Three categories of variables are specified in the ―Core‖ column in the domain models:





A Required variable is any variable that is basic to the identification of a data record (i.e., essential key
variables and a topic variable) or is necessary to make the record meaningful. Required variables must always
be included in the dataset and cannot be null for any record.
An Expected variable is any variable necessary to make a record useful in the context of a specific domain.
Expected variables may contain some null values, but in most cases will not contain null values for every record.
When no data has been collected for an expected variable, however, a null column should still be included in the
dataset, and a comment should be included in the define.xml to state that data was not collected.
A Permissible variable should be used in a domain as appropriate when collected or derived. Except where
restricted by specific domain assumptions, any SDTM Timing and Identifier variables, and any Qualifier
variables from the same general observation class are permissible for use in a domain based on that general
observation class. The Sponsor can decide whether a Permissible variable should be included as a column
when all values for that variable are null. The sponsor does not have the discretion to not submit permissible
variables when they contain data.

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CDISC SDTM Implementation Guide (Version 3.1.2)

4.1.1.6 ADDITIONAL GUIDANCE ON DATASET NAMING
SDTM datasets are normally named to be consistent with the domain code; for example, the Demographics dataset
(DM) is named dm.xpt (see Appendix C2 for a list of standard and reserved domain codes). Exceptions to this rule
are described in Section 4.1.1.7 for general-observation-class datasets and in Section 8 for the RELREC and SUPP-datasets.
317H

318H

319H

In some cases, sponsors may need to define new custom domains other than those represented in the SDTMIG or
listed in Appendix C2, and may be concerned that CDISC domain codes defined in the future will conflict with
those they choose to use. To eliminate any risk of a sponsor using a name that CDISC later determines to have a
different meaning, domain codes beginning with the letters X, Y, or Z have been reserved for the creation of custom
domains. Any letter or number may be used in the second position. Note the use of codes beginning with X, Y, or Z
is optional, and not required for custom domains.
320H

4.1.1.7 SPLITTING DOMAINS
Sponsors may choose to split a domain of topically related information into physically separate datasets. In such
cases, one of two approaches should be implemented:
1) For a domain based on a general observation class, splitting should be according to values in --CAT (which
must not be null).
2) The Findings About (FA) domain (Section 6.4) can be split either by --CAT values (per the bullet above) or
relative to the parent domain of the value in --OBJ. For example, FACM would store Findings About CM
records. See Section 6.4.2 for more details.
321H

32H

The following rules must be adhered to when splitting a domain into separate datasets to ensure they can be
appended back into one domain dataset:
1) The value of DOMAIN must be consistent across the separate datasets as it would have been if they had not
been split (e.g., QS, FA).
2) All variables that require a domain prefix (e.g., --TESTCD, --LOC) must use the value of DOMAIN as the
prefix value (e.g., QS, FA).
3) --SEQ must be unique within USUBJID for all records across all the split datasets. If there are 1000 records
for a USUBJID across the separate datasets, all 1000 records need unique values for --SEQ.
4) When relationship datasets (e.g., SUPPxx, FAxx, CO, RELREC) relate back to split parent domains, IDVAR
should generally be --SEQ. When IDVAR is a value other than --SEQ (e.g., --GRPID, --REFID, --SPID),
care should be used to ensure that the parent records across the split datasets have unique values for the
variable specified in IDVAR, so that related children records do not accidentally join back to incorrect parent
records.
5) Variables of the same name in separate datasets should have the same SAS Length attribute to avoid any
difficulties if the sponsor or FDA should decide to append datasets together.
6) Permissible variables included in one split dataset need not be included in all split datasets. Should the
datasets be appended in SAS, permissible variables not used in some split datasets will have null values in
the appended datasets. Care is advised, however, when considering variable order. Should a permissible
variable used in one (or more) split datasets not be included in the first dataset used in a SAS Set statement,
the order of variables could be compromised.
7) Split dataset names can be up to four characters in length. For example, if splitting by --CAT, then dataset
names would be the domain name plus up to two additional characters (e.g., QS36 for SF-36). If splitting
Findings About by parent domain, then the dataset name would be the domain name plus the two-character
domain code describing the parent domain code (e.g., FACM). The four-character dataset-name limitation
allows the use of a Supplemental Qualifier dataset associated with the split dataset.
8) Supplemental Qualifier datasets for split domains would also be split. The nomenclature would include the
additional one-to-two characters used to identify the split dataset (e.g., SUPPQS36, SUPPFACM). The value
of RDOMAIN in the SUPP-- datasets would be the two-character domain code (e.g., QS, FA).
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CDISC SDTM Implementation Guide (Version 3.1.2)
9) In RELREC, if a dataset-level relationship is defined for a split Findings About domain, then RDOMAIN
may contain the four-character dataset name, as shown in the following example.
STUDYID

RDOMAIN

ABC

FACM

USUBJID

IDVAR

IDVARVAL

RELTYPE

RELID

CMSPID

ONE

FASPID

MANY

10) See the SDTM Metadata Implementation Guide for guidance on how to represent the metadata for a set of
split domain datasets in the define.xml.
Note that submission of split SDTM domains may be subject to additional dataset splitting conventions as defined
by regulators via technical specifications (e.g., Study Data Specifications) and/or as negotiated with regulatory
reviewers.
4.1.1.7.1 EXAMPLE OF SPLITTING QUESTIONNAIRES
This example shows the split QS domain data into three datasets: Clinical Global Impression (QSCG), Cornell Scale
for Depression in Dementia (QSCS) and Mini Mental State Examination (QSMM). Each dataset represents a subset
of the QS domain data and has only one value of QSCAT.
QS Domains
qscg.xpt (Clinical Global Impressions)
Row
1

STUDYID
CDISC01

DOMAIN
QS

USUBJID
CDISC01.100008

QSSEQ
1

CDISC01

CDISC01.100008

CDISC01

CDISC01.100014

CDISC01

CDISC01.100014

Row
1
(cont)
2
(cont)
3
(cont)
4
(cont)

QSBLFL

QSSPID
CGICGI-I
CGICGI-I
CGICGI-I
CGICGI-I

QSORRES
No change

QSSTRESC
4

QSSTRESN
4

Much
Improved
Minimally
Improved
Minimally
Improved

QSTESTCD
CGIGLOB
CGIGLOB
CGIGLOB
CGIGLOB

VISITNUM
3

VISIT
WEEK
2
WEEK
24
WEEK
2
WEEK
24

QSTEST
Global
Improvement
Global
Improvement
Global
Improvement
Global
Improvement
VISITDY
15
169
15
169

QSCAT
Clinical Global
Impressions
Clinical Global
Impressions
Clinical Global
Impressions
Clinical Global
Impressions

QSDTC
200305-13
200310-13
200310-31
200403-30

QSDY
15

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168
17
168

CDISC SDTM Implementation Guide (Version 3.1.2)

qscs.xpt (Cornell Scale for Depression in Dementia)
Row
1

STUDYID
CDISC01

DOMAIN
QS

CDISC01

Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)

QSORRES
Severe
Severe
Severe
Mild

USUBJID
CDISC01.
100008
CDISC01.
100008
CDISC01.
100014
CDISC01.
100014

QSSEQ
3

QSSPID
CSDD-01

QSTESTCD
CSDD01

QSTEST
Anxiety

CSDD-01

CSDD01

Anxiety

CSDD-01

CSDD01

Anxiety

CSDD-06

CSDD06

Retardation

QSCAT
Cornell Scale for
Depression in Dementia
Cornell Scale for
Depression in Dementia
Cornell Scale for
Depression in Dementia
Cornell Scale for
Depression in Dementia

QSSTRESC QSSTRESN QSBLFL VISITNUM
VISIT
VISITDY QSDTC QSDY
2
2
1
SCREEN
-13
2003-04-15
-14
2
2
Y
2
BASELINE
1
2003-04-29
1
2
2
1
SCREEN
-13
2003-10-06
-9
1
1
Y
2
BASELINE
1
2003-10-15
1

qsmm.xpt (Mini Mental State Examination)
Row

STUDYID

DOMAIN

USUBJID

QSSEQ

QSSPID

QSTESTCD

QSTEST

CDISC01

MMSE-A.1

MMSEA1

CDISC01

MMSE-A.1

MMSEA1

CDISC01

MMSE-A.1

MMSEA1

CDISC01

CDISC01.
100008
CDISC01.
100008
CDISC01.
100014
CDISC01.
100014

MMSE-A.1

MMSEA1

Orientation Time
Score
Orientation Time
Score
Orientation Time
score
Orientation Time
score

Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)

QSCAT
Mini Mental State
Examination
Mini Mental State
Examination
Mini Mental State
Examination
Mini Mental State
Examination

QSORRES QSSTRESC QSSTRESN QSBLFL VISITNUM
VISIT
VISITDY QSDTC QSDY
4
4
4
1
SCREEN
-13
2003-04-15
-14
3
3
3
Y
2
BASELINE
1
2003-04-29
1
2
2
2
1
SCREEN
-13
2003-10-06
-9
2
2
2
Y
2
BASELINE
1
2003-10-15
1

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CDISC SDTM Implementation Guide (Version 3.1.2)
SUPPQS Domains
suppqscg.xpt: Supplemental Qualifiers for QSCG
Row
1
2

STUDYID RDOMAIN USUBJID
CDISC01
QS
CDISC01.
100008
CDISC01
QS
CDISC01.
100014

IDVAR
IDVARVAL QNAM QLABEL
QVAL QORIG QEVAL
QSCAT Clinical Global QSLANG Questionnaire GERMAN CRF
Impressions
Language
QSCAT Clinical Global QSLANG Questionnaire FRENCH CRF
Impressions
Language

suppqscs.xpt: Supplemental Qualifiers for QSCS
Row
1

STUDYID RDOMAIN
CDISC01
QS

USUBJID
CDISC01.
100008

CDISC01

CDISC01.
100014

IDVAR
IDVARVAL QNAM QLABEL
QVAL QORIG QEVAL
QSCAT Cornell Scale QSLANG Questionnaire GERMAN CRF
for Depression
Language
in Dementia
QSCAT Cornell Scale QSLANG Questionnaire FRENCH CRF
for Depression
Language
in Dementia

suppqsmm.xpt: Supplemental Qualifiers for QSMM
Row
1
2

STUDYID RDOMAIN
CDISC01
QS
CDISC01

USUBJID
CDISC01.
100008
CDISC01.
100014

IDVAR
IDVARVAL
QNAM QLABEL
QVAL QORIG QEVAL
QSCAT Mini Mental State QSLANG Questionnaire GERMAN CRF
Examination
Language
QSCAT Mini Mental State QSLANG Questionnaire FRENCH CRF
Examination
Language

4.1.1.8 ORIGIN METADATA
4.1.1.8.1 ORIGIN METADATA FOR VARIABLES
The Origin column of the define.xml is used to indicate where the data originated. Its purpose is to unambiguously
communicate to the reviewer whether data was collected on a CRF (and thus should be traceable to an annotated
CRF), derived (and thus traceable to some derivation algorithm), or assigned by some subjective process (and thus
traceable to some external evaluator). The SDTMIG defines the following controlled terms for specifying Origin:
CRF: The designation of ‖CRF‖ (along with a reference) as an origin in the define.xml means that data was
collected as part of a CRF and that there is an annotated CRF associated with the variable. Sponsors may specify
additional details about the origin that may be helpful to the Reviewer (e.g., electronic diary) in the Comments
section of the define.xml. An origin of ―CRF‖ includes information that is preprinted on the CRF (e.g.,
―RESPIRATORY SYSTEM DISORDERS‖ for MHCAT).
eDT: The designation of "eDT" as an origin in the define.xml means that the data are received via an electronic Data
Transfer (eDT) and usually does not have associated annotations. An origin of eDT refers to data collected via data
streams such as laboratory, ECG, or IVRS. Sponsors may specify additional details about the origin that may be
helpful to the Reviewer in the Comments section of the define.xml.
Derived: Derived data are not directly collected on the CRF but are calculated by an algorithm or reproducible rule,
which is dependent upon other data values. This algorithm is applied across all values and may reference other
SDTM datasets. The derivation is assumed to be performed by the Sponsor. This does not apply to derived lab test
results performed directly by labs (or by devices).

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Examples illustrating the distinction between collected and derived values include the following:
 A value derived by an eCRF system from other entered fields has an origin of "Derived, " since the sponsor
controls the derivation.
 A value derived from collected data by the sponsor, or a CRO working on their behalf, has an origin of
"Derived."
 A value derived by an investigator and written/entered on a CRF has an origin of "CRF" (along with a
reference) rather than ―derived‖.
 A value derived by a vendor (e.g., a central lab) according to their procedures is considered collected rather
than derived, and would have an origin of ―eDT‖.
Assigned: A value that is determined by individual judgment (by an evaluator other than the subject or investigator),
rather than collected as part of the CRF or derived based on an algorithm. This may include third party attributions
by an adjudicator. Coded terms that are supplied as part of a coding process (as in --DECOD) are considered to have
an Origin of ―Assigned‖. Values that are set independently of any subject-related data values in order to complete
SDTM fields such as DOMAIN and --TESTCD are considered to have an Origin of ―Assigned‖.
Protocol: A value that is defined as part of the Trial Design preparation (see Section 7). An example would be
VSPOS (Vital Signs Position), which may be specified only in the protocol and not appear on a CRF.
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The term ―Sponsor Defined‖ was used in earlier versions of the SDTMIG to advise the Sponsor to supply the
appropriate Origin value in the metadata. The text ―Sponsor Defined‖ was not intended to be used in the define.xml
and is no longer used in V3.1.2 and later.
4.1.1.8.2 ORIGIN METADATA FOR RECORDS
Sponsors are cautioned to recognize that an Origin of ―Derived‖ means that all values for that variable were derived,
and that ―CRF‖ (along with a reference) means that all were collected. In some cases, both collected and derived
values may be reported in the same field. For example, some records in a Findings dataset such as QS contain values
collected from the CRF and other records may contain derived values such as a total score. When both derived and
collected values are reported in a field, the value-level metadata origin will indicate at the test level if the value is
―Derived‖ or ―CRF‖ and the variable-level metadata origin will list all types for that variable separated by commas
(e.g., ―Derived, CRF‖).
4.1.1.9 ASSIGNING NATURAL KEYS IN THE METADATA
Section 3.2 indicates that a sponsor should include in the metadata the variables that contribute to the natural key for
a domain. The following examples are illustrations of how to do this, and include a case where a Supplemental
Qualifier variable is referenced because it forms part of the natural key.
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Physical Examination (PE) domain example:
Sponsor A chooses the following natural key for the PE domain:
STUDYID, USUBJID, VISTNUM, PETESTCD
Sponsor B collects data in such a way that the location (PELOC) and method (PEMETHOD) variables need to be
included in the natural key to identify a unique row, but they do not collect a visit variable; instead they use the visit
date (PEDTC) to sequence the data. Sponsor B then defines the following natural key for the PE domain.
STUDYID, USUBJID, PEDTC, PETESTCD, PELOC, PEMETHOD
In certain instances a Supplemental Qualifier variable (i.e., a QNAM value, see Section 8.4) might also contribute to
the natural key of a record, and therefore needs to be referenced as part of the natural key for a domain. The
important concept here is that a domain is not limited by physical structure. A domain may be comprised of more
than one physical dataset, for example the main domain dataset and its associated Supplemental Qualifiers dataset.
Supplemental Qualifiers variables should be referenced in the natural key by using a two-part name. The word
QNAM must be used as the first part of the name to indicate that the contributing variable exists in a dataset (and
this can be either a domain-specific SUPP-- dataset or the general SUPPQUAL dataset) and the second part is the
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value of QNAM that ultimately becomes a column reference (e.g., QNAM.XVAR when the SUPP-- record has a
QNAM of ―XVAR‖) when the SUPPQUAL records are joined on to the main domain dataset.
Continuing with the PE domain example above, Sponsor B might have used ultrasound as a method of measurement
and might have collected additional information such as the makes and models of ultrasound equipment employed.
The sponsor considers the make and model information to be essential data that contributes to the uniqueness of the
test result, and so creates Supplemental Qualifier variables for make (QNAM=PEMAKE) and model
(QNAM=PEMODEL). The natural key is then defined as follows:
STUDYID, USUBJID, PEDTC, PETESTCD, PELOC, PEMETHOD, QNAM.PEMAKE, QNAM.PEMODEL
This approach becomes very useful in a Findings domain when a sponsor might choose to employ generic
--TESTCD values rather than compound --TESTCD values. The use of generic test codes helps to create distinct
lists of manageable controlled terminology for --TESTCD. In studies where multiple repetitive tests or
measurements are being made, for example in a rheumatoid arthritis study where repetitive measurements of bone
erosion in the hands and wrists might be made using both X-ray and MRI equipment, one approach to recording this
data might be to create an individual --TESTCD value for each measurement. Taking just the phalanges, a sponsor
might want to express the following in a test code in order to make it unique:
 Left or Right hand
 Phalange position (proximal / distal / middle)
 Rotation of the hand
 Method of measurement (X-ray / MRI)
 Machine Make
 Machine Model
Trying to encapsulate all of this information within a unique value of a --TESTCD is not a recommended approach
for the following reasons:
 It results in the creation of a potentially large number of test codes
 The eight-character values of --TESTCD becoming less intuitively meaningful
 Multiple test codes are essentially representing the same test or measurement simply to accommodate
attributes of a test within the --TESTCD value itself (e.g., to represent a body location at which a
measurement was taken).
As a result, the preferred approach would be to use a generic (or simple) test code that requires associated qualifier
variables to fully express the test detail. Using this approach in the above example, a generic --TESTCD value might
be ―EROSION‖ and the additional components of the compound test codes discussed above would be represented in
a number of distinct qualifier variables. These may include domain variables (--LOC, --METHOD, etc.) and
Supplemental Qualifier variables (QNAM.MAKE, QNAM.MODEL, etc.). Expressing the natural key becomes very
important in this situation in order to communicate the variables that contribute to the uniqueness of a test.
If a generic --TESTCD was used the following variables would be used to fully describe the test. The test is
―EROSION‖, the location is ―Left MCP I‖, the method of measurement is ―Ultrasound‖, the make of the ultrasound
machine is ―ACME‖ and the model of the ultrasound machine is ―u 2.1‖. This domain includes both domain
variables and Supplemental Qualifier variables that contribute to the natural key of each row and to describe the
uniqueness of the test.

--TESTCD
EROSION

--TEST
EROSION

--LOC
LEFT MCP I

--METHOD
ULTRASOUND

QNAM.MAKE
ACME

QNAM.MODEL
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4.1.2 GENERAL VARIABLE ASSUMPTIONS
4.1.2.1 VARIABLE-NAMING CONVENTIONS
SDTM variables are named according to a set of conventions, using fragment names (defined in Appendix D).
Variables with names ending in ―CD‖ are ―short‖ versions of associated variables that do not include the ―CD‖
suffix (e.g., --TESTCD is the short version of --TEST).
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Values of --TESTCD must be limited to 8 characters, and cannot start with a number, nor can they contain characters
other than letters, numbers, or underscores. This is to avoid possible incompatibility with SAS V5 Transport files.
This limitation will be in effect until the use of other formats (such as XML) becomes acceptable to regulatory
authorities.
QNAM serves the same purpose as --TESTCD within supplemental qualifier datasets, and so values of QNAM are
subject to the same restrictions as values of --TESTCD.
Values of other ―CD‖ variables are not subject to the same restrictions as --TESTCD.
 ETCD (the companion to ELEMENT) and TSPARMCD (the companion to TSPARM) are limited to 8
characters and do not have special character restrictions. These values should be short for ease of use in
programming, but it is not expected that they will need to serve as variable names.
 ARMCD is limited to 20 characters and does not have special character restrictions. The maximum length
of ARMCD is longer than for other ―short‖ variables to accommodate the kind of values that are likely to
be needed for crossover trials. For example, if ARMCD values for a seven-period crossover were
constructed using two-character abbreviations for each treatment and separating hyphens, the length of
ARMCD values would be 20.
Variable descriptive names (labels), up to 40 characters, should be provided as data variable labels.
Use of variable names (other than domain prefixes), formats, decodes, terminology, and data types for the same type
of data (even for custom domains and Supplemental Qualifiers) should be consistent within and across studies
within a submission. Sponsors must use the predefined SDTM-standard labels in all standard domains.
4.1.2.2 TWO-CHARACTER DOMAIN IDENTIFIER
In order to minimize the risk of difficulty when merging/joining domains for reporting purposes, the two-character
Domain Identifier is used as a prefix in most variable names.
Special-Purpose domains (see Section 5), Standard domains (see Section 6), Trial Design domains (see Section 7)
and Relationship datasets (see Section 8) already specify the complete variable names, so no action is required.
When creating custom domains based on the General Observation Classes, sponsors must replace the -- (two
hyphens) prefix in the General Observation Class, Timing, and Identifier variables with the two-character Domain
Identifier (DOMAIN) variable value for that domain/dataset. The two-character domain code is limited to A to Z for
the first character, and A-Z, 0 to 9 for the 2nd character. No special characters are allowed for compatibility with SAS
version 5 transport files, and with file naming for the Electronic Common Technical Document (eCTD).
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The philosophy applied to determine which variable names use a prefix was that all variable names are prefixed with
the Domain Identifier in which they originate except the following:
a. Required Identifiers (STUDYID, DOMAIN, USUBJID)
b. Commonly used grouping and merge Keys (VISIT, VISITNUM, VISITDY), and many of the variables
in trial design (such as ELEMENT and ARM)
c. All Demographics domain (DM) variables other than DMDTC and DMDY
d. All variables in RELREC and SUPPQUAL, and some variables in Comments and Trial Design datasets.
Required Identifiers are not prefixed because they are usually used as keys when merging/joining observations. The
--SEQ and the optional Identifiers --GRPID and --REFID are prefixed because they may be used as keys when
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4.1.2.3 USE OF “SUBJECT” AND USUBJID
―Subject‖ should be used where applicable to generically refer to both ―patients‖ and ―healthy volunteers‖ in order
to be consistent with the recommendation in FDA guidance. The term ―Subject‖ should be used consistently in all
labels and comments. To identify a subject uniquely across all studies for all applications or submissions involving
the product, a unique identifier (USUBJID) should be assigned and included in all datasets.
The unique subject identifier (USUBJID) is required in all datasets containing subject-level data. USUBJID values
must be unique for each trial participant (subject) across all trials in the submission. This means that no two (or
more) subjects, across all trials in the submission, may have the same USUBJID. Additionally, the same person who
participates in multiple clinical trials (when this is known) must be assigned the same USUBJID value in all trials.
Sample Rows from individual study dm.xpt files for a same subject that participates first in ACME01 study, then
ACME14 study. Note that this is only one example of the possible values for USUBJID. CDISC does not
recommend any specific format for the values of USUBJID, only that the values need to be unique for all subjects in
the submission, and across multiple submissions for the same compound. Many sponsors concatenate values for the
Study, Site and Subject into USUBJID, but this is not a requirement. It is acceptable to use any format for
USUBJID, as long as the values are unique across all subjects per FDA guidance.
Study ACME01 dm.xpt
STUDYID
DOMAIN
ACME01
DM

USUBJID
ACME01-05-001

SUBJID
001

SITEID
05

INVNAM
John Doe

Study ACME14 dm.xpt
STUDYID
DOMAIN
ACME14
DM

USUBJID
ACME01-05-001

SUBJID
017

SITEID
14

INVNAM
Mary Smith

4.1.2.4 CASE USE OF TEXT IN SUBMITTED DATA
It is recommended that text data be submitted in upper case text. Exceptions may include long text data (such as
comment text); values of --TEST in Findings datasets (which may be more readable in title case if used as labels in
transposed views); and certain controlled terminology (see Section 4.1.3.2) that are already in mixed case. The
Sponsor‘s define.xml may indicate as a general note or assumption whether case sensitivity applies to text data for
any or all variables in the dataset.
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4.1.2.5 CONVENTION FOR MISSING VALUES
Missing values for individual data items should be represented by nulls. This is a change from previous versions of
the SDTMIG, which allowed sponsors to define their conventions for missing values. Conventions for representing
observations not done using the SDTM --STAT and --REASND variables are addressed in Section 4.1.5.1.2 and the
individual domain models.
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4.1.2.6 GROUPING VARIABLES AND CATEGORIZATION
Grouping variables are Identifiers and Qualifiers that group records in the SDTM domains/datasets such as the
--CAT (Category) and --SCAT (Subcategory) variables assigned by sponsors to categorize topic-variable values. For
example, a lab record with LBTEST = ―SODIUM‖ might have LBCAT = ―CHEMISTRY‖ and LBSCAT =
―ELECTROLYTES‖. Values for --CAT and --SCAT should not be redundant with the domain name or dictionary
classification provided by --DECOD and --BODSYS.
1. Hierarchy of Grouping Variables
STUDYID
DOMAIN
--CAT
--SCAT
USUBJID
--GRPID
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
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2. How Grouping Variables Group Data
A. For the subject
1.

All records with the same USUBJID value are a group of records that describe that subject.

B. Across subjects (records with different USUBJID values)
1.

All records with the same STUDYID value are a group of records that describe that study

All records with the same DOMAIN value are a group of records that describe that domain

--CAT (Category) and --SCAT (Sub-category) values further subset groups within the domain.
Generally, --CAT/--SCAT values have meaning within a particular domain. However, it is
possible to use the same values for --CAT/--SCAT in related domains (e.g., MH and AE). When
values are used across domains, the meanings should be the same. Examples of where
--CAT/--SCAT may have meaning across domains/datasets:
a. Some limited cases where they will have meaning across domains within the same
general observation class, because those domains contain similar conceptual information.
Adverse Events (AE), Medical History (MH) and Clinical Events (CE), for example, are
conceptually the same data, the only differences being when the event started relative to
the study start and whether the event is considered a regulatory reportable adverse event
in the study. Neurotoxicities collected in Oncology trials both as separate Medical
History CRFs (MH domain) and Adverse Event CRFs (AE domain) could both
identify/collect ―Paresthesia of the left Arm.‖ In both domains, the --CAT variable could
have the value of NEUROTOXICITY.
b. Cases where multiple datasets are necessary to capture data in the same domain. As an
example, perhaps the existence, start and stop date of ―Paresthesia of the left Arm‖ is
reported as an Adverse Event (AE domain), but the severity of the event is captured at
multiple visits and recorded as Findings About (FA dataset). In both cases the --CAT
variable could have a value of NEUROTOXICITY.
c. Cases where multiple domains are necessary to capture data that was collected together and
have an implicit relationship, perhaps identified in the Related Records (RELREC) special
purpose dataset. Stress Test data collection, for example, may capture the following:
i. Information about the occurrence, start, stop, and duration of the test in an
Events or Interventions custom general observation class dataset
ii. Vital Signs recorded during the stress test (VS domain)
iii. Treatments (e.g., oxygen) administered during the stress test (in an Interventions
domain).
In such cases, the data collected during the stress tests recorded in three separate domains
may all have --CAT/--SCAT values (STRESS TEST) that identify this data was collected
during the stress test.

C. Within subjects (records with the same USUBJID values)
1. --GRPID values further group (subset) records within USUBJID. All records in the same domain with the
same --GRPID value are a group of records within USUBJID. Unlike --CAT and --SCAT, --GRPID values
are not intended to have any meaning across subjects and are usually assigned during or after data collection.
2. Although --SPID and --REFID are Identifier variables, usually not considered to be grouping variables, they
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3. Differences between Grouping Variables
A. The primary distinctions between --CAT/--SCAT and --GRPID are:
 --CAT/--SCAT are known (identified) about the data before it is collected
 --CAT/--SCAT values group data across subjects
 --CAT/--SCAT may have some controlled terminology
 --GRPID is usually assigned during or after data collection at the discretion of the Sponsor
 --GRPID groups data only within a subject
 --GRPID values are sponsor-defined, and will not be subject to controlled terminology.
Therefore, data that would be the same across subjects is usually more appropriate in --CAT/--SCAT, and
data that would vary across subjects is usually more appropriate in --GRPID. For example, a Concomitant
Medication administered as part of a known combination therapy for all subjects (Mayo Clinic Regimen
for example) would more appropriately use --CAT/--SCAT to identify the medication as part of that
regimen. Groups of medications taken to treat an SAE, recorded in/on the SAE collection, and could be
part of a different grouping of medications for each subject would more appropriately use --GRPID.
In domains based on the Findings general observation class, the --RESCAT variable can be used to categorize results
after the fact. --CAT and --SCAT by contrast, are generally pre-defined by the Sponsor or used by the investigator at
the point of collection, not after assessing the value of Findings results.
4.1.2.7 SUBMITTING FREE TEXT FROM THE CRF
Sponsors often collect free text data on a CRF to supplement a standard field. This often occurs as part of a list of
choices accompanied by ―Other, specify.‖ The manner in which these data are submitted will vary based on their
role.
4.1.2.7.1 “SPECIFY” VALUES FOR NON-RESULT QUALIFIER VARIABLES
When free-text information is collected to supplement a standard non-result Qualifier field, the free-text value
should be placed in the SUPP-- dataset described in Section 8.4. When applicable, controlled terminology should be
used for SUPP-- field names (QNAM) and their associated labels (QLABEL) (see Section 8.4 and Appendix C5).
For example, when a description of "Other Medically Important Serious Adverse Event" category is collected on a
CRF, the free text description should be stored in the SUPPAE dataset.
 AESMIE=Y
 SUPPAE QNAM=AESOSP, QLABEL= Other Medically Important SAE, QVAL=HIGH RISK FOR
ADDITIONAL THROMBOSIS
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Another example is a CRF that collects reason for dose adjustment with additional free-text description:
Reason for Dose Adjustment (EXADJ)
Adverse event
Insufficient response
Non-medical reason

Describe
_____________________
_____________________
_____________________

The free text description should be stored in the SUPPEX dataset.
 EXADJ=NONMEDICAL REASON
 SUPPEX QNAM=EXADJDSC, QLABEL= Reason For Dose Adjustment, QVAL=PATIENT
MISUNDERSTOOD INSTRUCTIONS
 Note that QNAM references the ―parent‖ variable name with the addition of ―OTH, ‖ one of the standard variable
naming fragments for ―Other‖ (see Appendix D). Likewise, the label is a modification of the parent variable label.
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When the CRF includes a list of values for a qualifier field that includes "Other" and the "Other" is supplemented
with a "Specify" free text field, then the manner in which the free text "Specify" value is submitted will vary based
on the sponsor's coding practice and analysis requirements. For example, consider a CRF that collects the
anatomical location of administration (EXLOC) of a study drug given as an injection:
Location of Injection
Right Arm
Left Arm
Right Thigh
Left Thigh
Other, Specify: _________________
An investigator has selected ―OTHER‖ and specified ―UPPER RIGHT ABDOMEN‖. Several options are available
for submission of this data:
1) If the sponsor wishes to maintain controlled terminology for the EXLOC field and limit the terminology to the 5
pre-specified choices, then the free text is placed in SUPPEX.
EXLOC
OTHER
QNAM
EXLOCOTH

QLABEL
Other Location of Dose Administration

QVAL
UPPER RIGHT ABDOMEN

2) If the sponsor wishes to maintain controlled terminology for EXLOC but will expand the terminology based on
values seen in the specify field, then the value of EXLOC will reflect the sponsor‘s coding decision and
SUPPEX could be used to store the verbatim text.
EXLOC
ABDOMEN
QNAM
QLABEL
QVAL
EXLOCOTH
Other Location of Dose Administration
UPPER RIGHT ABDOMEN
Note that the sponsor might choose a different value for EXLOC (e.g., UPPER ABDOMEN, TORSO)
depending on the sponsor's coding practice and analysis requirements.
3) If the sponsor does not require that controlled terminology be maintained and wishes for all responses to be
stored in a single variable, then EXLOC will be used and SUPPEX is not required.
EXLOC
UPPER RIGHT ABDOMEN
4.1.2.7.2 “SPECIFY” VALUES FOR RESULT QUALIFIER VARIABLES
When the CRF includes a list of values for a result field that includes "Other" and the "Other" is supplemented with
a "Specify" free text field, then the manner in which the free text "Specify" value is submitted will vary based on the
sponsor's coding practice and analysis requirements. For example, consider a CRF where the sponsor requests the
subject's eye color:
Eye Color
Brown
Black
Blue
Green
Other, specify: ________
An investigator has selected "OTHER" and specified "BLUEISH GRAY." As in the above discussion for non-result
Qualifier values, the sponsor has several options for submission:
1) If the sponsor wishes to maintain controlled terminology in the standard result field and limit the terminology to
the 5 pre-specified choices, then the free text is placed in --ORRES and the controlled terminology in
--STRESC.
 SCTEST=Eye Color, SCORRES=BLUEISH GRAY, SCSTRESC=OTHER
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2) If the sponsor wishes to maintain controlled terminology in the standard result field, but will expand the
terminology based on values seen in the specify field, then the free text is placed in --ORRES and the value of
--STRESC will reflect the sponsor's coding decision.
 SCTEST=Eye Color, SCORRES=BLUEISH GRAY, SCSTRESC=GRAY
3) If the sponsor does not require that controlled terminology be maintained, the verbatim value will be copied to
--STRESC.
 SCTEST=Eye Color, SCORRES=BLUEISH GRAY, SCSTRESC=BLUEISH GRAY
Note that rules for the use of ―Other, Specify‖ for the Result Qualifier variable, --OBJ, is discussed in Section 6.4.3.
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4.1.2.7.3 “SPECIFY” VALUES FOR TOPIC VARIABLES
Interventions: If a list of specific treatments is provided along with ―Other, Specify‖, --TRT should be populated with
the name of the treatment found in the specified text. If the sponsor wishes to distinguish between the pre-specified
list of treatments and those recorded under ―Other, Specify, ‖ the --PRESP variable could be used. For example:
Indicate which of the following concomitant medications was used to treat the subject‘s headaches:
Acetaminophen
Aspirin
Ibuprofen
Naproxen
Other: ______
If ibuprofen and diclofenac were reported, the CM dataset would include the following:
 CMTRT=IBUPROFEN, CMPRESP=Y
 CMTRT=DICLOFENAC, CMPRESP is null.
Events: ―Other, Specify‖ for Events may be handled similarly to Interventions. --TERM should be populated with
the description of the event found in the specified text and --PRESP could be used to distinguish between
pre-specified and free text responses.
Findings: ―Other, Specify‖ for tests may be handled similarly to Interventions. --TESTCD and --TEST should be
populated with the code and description of the test found in the specified text. If specific tests are not prespecified on
the CRF and the investigator has the option of writing free text for tests, then the name of the test would have to be
coded to ensure that all --TESTCD and --TEST values are controlled terminology and are not free text. For example,
a lab CRF has tests of Hemoglobin, Hematocrit and ―Other, specify‖. The value the investigator wrote for ―Other,
specify‖ is Prothrombin time with an associated result and units. The sponsor would submit the controlled
terminology for this test which is LBTESTCD = PT and LBTEST = Prothrombin Time.
4.1.2.8 MULTIPLE VALUES FOR A VARIABLE
4.1.2.8.1 MULTIPLE VALUES FOR AN INTERVENTION OR EVENT TOPIC VARIABLE
If multiple values are reported for a topic variable (i.e., --TRT in an Interventions general-observation-class dataset or
--TERM in an Events general-observation-class dataset), it is assumed that the sponsor will split the values into
multiple records or otherwise resolve the multiplicity as per the sponsor‘s standard data management procedures. For
example, if an adverse event term of ―Headache and Nausea‖ or a concomitant medication of ―Tylenol and Benadryl‖ is
reported, sponsors will often split the original report into separate records and/or query the site for clarification. By the
time of submission, the datasets should be in conformance with the record structures described in the SDTMIG. Note
that the Disposition dataset (DS) is an exception to the general rule of splitting multiple topic values into separate
records. For DS, one record for each disposition or protocol milestone is permitted according to the domain structure.
For cases of multiple reasons for discontinuation see Section 6.2.2.1, Assumption 5 for additional information.
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4.1.2.8.2 MULTIPLE VALUES FOR A FINDINGS RESULT VARIABLE
If multiple result values (--ORRES) are reported for a test in a Findings class dataset, multiple records should be
submitted for that --TESTCD. Example:
 EGTESTCD=RHYRATE, EGTEST=Rhythm and Rate, EGORRES=ATRIAL FIBRILLATION
 EGTESTCD=RHYRATE, EGTEST=Rhythm and Rate, EGORRES=ATRIAL FLUTTER
Note that in this case, the sponsor‘s operational database may have a result-sequence variable as part of the natural key.
Some sponsors may elect to keep this variable in a Supplemental Qualifier record, while others may decide to use
--SPID or --SEQ to replace it. Dependent variables such as result Qualifiers should never be part of the natural key.
4.1.2.8.3 MULTIPLE VALUES FOR A NON-RESULT QUALIFIER VARIABLE
The SDTM permits one value for each Qualifier variable per record. If multiple values exist (e.g., due to a ―Check all that
apply‖ instruction on a CRF), then the value for the Qualifier variable should be ―MULTIPLE‖ and SUPP-- should be
used to store the individual responses. It is recommended that the SUPP-- QNAM value reference the corresponding
standard domain variable with an appended number or letter. In some cases, the standard variable name will be shortened
to meet the 8 character variable name requirement or it may be clearer to append a meaningful character string as shown in
the 2nd AE example below where the 1st 3 characters of the drug name are appended. Likewise the QLABEL value should
be similar to the standard label. The values stored in QVAL should be consistent with the controlled terminology
associated with the standard variable. See Section 8.4 for additional guidance on maintaining appropriately unique QNAM
values. The following example includes selected variables from the ae.xpt and suppae.xpt datasets for a rash whose
locations are the face, neck, and chest.
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AE Dataset
AETERM
RASH

AELOC
MULTIPLE

SUPPAE Dataset
QNAM
QLABEL
AELOC1
Location of the Reaction 1
AELOC2
Location of the Reaction 2
AELOC3
Location of the Reaction 3

QVAL
FACE
NECK
CHEST

In some cases, values for QNAM and QLABEL more specific than those above may be needed. For example, a
sponsor might conduct a study with two study drugs (e.g., open-label study of Abcicin + Xyzamin), and may require
the investigator assess causality and describe action taken for each drug for the rash:
AE Dataset
AETERM
RASH

AEREL
MULTIPLE

SUPPAE Dataset
QNAM
AERELABC
AERELXYZ
AEACNABC
AEACNXYZ

QLABEL
Causality of Abcicin
Causality of Xyzamin
Action Taken with Abcicin
Action Taken with Xyzamin

AEACN
MULTIPLE

QVAL
POSSIBLY RELATED
UNLIKELY RELATED
DOSE REDUCED
DOSE NOT CHANGED

In each of the above examples, the use of SUPPAE should be documented in the metadata and the annotated CRF.
The controlled terminology used should be documented as part of value-level metadata.

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If the sponsor has clearly documented that one response is of primary interest (e.g., in the CRF, protocol, or analysis
plan), the standard domain variable may be populated with the primary response and SUPP-- may be used to store
the secondary response(s). For example, if Abcicin is designated as the primary study drug in the example above:
AE Dataset
AETERM
RASH

AEREL
POSSIBLY RELATED

AEACN
DOSE REDUCED

SUPPAE Dataset
QNAM
QLABEL
AERELX
Causality of Xyzamin
AEACNX
Action Taken with Xyzamin

QVAL
UNLIKELY RELATED
DOSE NOT CHANGED

Note that in the latter case the label for standard variables AEREL and AEACN will have no indication that they
pertain to Abcicin. This association must be clearly documented in the metadata and annotated CRF.

4.1.3 CODING AND CONTROLLED TERMINOLOGY ASSUMPTIONS
PLEASE NOTE: Examples provided in the column “CDISC Notes” are only examples and not intended to imply
controlled terminology. Please check current controlled terminology at this link:
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
37H156

4.1.3.1 TYPES OF CONTROLLED TERMINOLOGY
For SDTMIG V3.1.1 the presence of a single asterisk (*) or a double asterisk (**) in the “Controlled Terms or
Format” column indicated that a discrete set of values (controlled terminology) was expected for the variable. This
set of values was sponsor-defined in cases where standard vocabularies had not yet been defined (represented by *)
or from an external published source such as MedDRA (represented by **). For V3.1.2, controlled terminology is
now represented one of three ways:
• A single asterisk when there is no specific CT available at the current time, but the SDS Team expects
that sponsors may have their own CT and/or the CDISC Controlled Terminology Team may be
developing CT.
• A list of controlled terms for the variable when values are not yet maintained externally
• The name of an external codelist whose values can be found via the hyperlinks in either the domain or
Appendix C.
In addition, the “Controlled Terms or Format” column has been used to indicate a common format such as ISO
8601.
4.1.3.2 CONTROLLED TERMINOLOGY TEXT CASE
It is recommended that controlled terminology be submitted in upper case text for all cases other than those
described as exceptions below. Deviations to this rule should be described in the define.xml.
a.
b.

If the external reference for the controlled terminology is not in upper case then the data should
conform to the case prescribed in the external reference (e.g., MedDRA and LOINC).
Units, which are considered symbols rather than abbreviated text (e.g., mg/dL).

4.1.3.3 CONTROLLED TERMINOLOGY VALUES
The controlled terminology or a link to the controlled terminology should be included in the define.xml wherever
applicable. All values in the permissible value set for the study should be included, whether they are represented in
the submitted data or not. Note that a null value should not be included in the permissible value set. A null value is
implied for any list of controlled terms unless the variable is “Required” (see Section 4.1.1.5).
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4.1.3.4 USE OF CONTROLLED TERMINOLOGY AND ARBITRARY NUMBER CODES
Controlled terminology or decoded text should be used instead of arbitrary number codes in order to reduce
ambiguity for submission reviewers. For example, for concomitant medications, the verbatim term and/or dictionary
term should be presented, rather than numeric codes. Separate code values may be submitted as Supplemental
Qualifiers and may be necessary in analysis datasets.
4.1.3.5 STORING CONTROLLED TERMINOLOGY FOR SYNONYM QUALIFIER VARIABLES




For events such as AEs and Medical History, populate --DECOD with the dictionary‘s preferred term and
populate --BODSYS with the preferred body system name. If a dictionary is multi-axial, the value in
--BODSYS should represent the system organ class (SOC) used for the sponsor‘s analysis and summary
tables, which may not necessarily be the primary SOC.
For concomitant medications, populate CMDECOD with the drug's generic name and populate CMCLAS
with the drug class used for the sponsor‘s analysis and summary tables. If coding to multiple classes, follow
assumption 4.1.2.8.1 or omit CMCLAS.
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In either case, no other intermediate levels (e.g., MedDRA LLT, HLT, HLGT) or relationships should be stored in
the dataset. These may be provided in a Supplemental Qualifiers dataset (see Section 8.4 and Appendix C5 for more
information). By knowing the dictionary and version used, the reviewer will be able to obtain intermediate levels in
a hierarchy (as in MedDRA), or a drug‘s ATC codes (as in WHO Drug). The sponsor is expected to provide the
dictionary name and version used to map the terms by utilizing the define.xml external codelist attributes.
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4.1.3.6 STORING TOPIC VARIABLES FOR GENERAL DOMAIN MODELS
The topic variable for the Interventions and Events general-observation-class models is often stored as verbatim text.
For an Events domain, the topic variable is --TERM. For an Interventions domain, the topic variable is --TRT. For a
Findings domain, the topic variable, --TESTCD, should use Controlled Terminology (e.g., SYSBP for Systolic
Blood Pressure). If CDISC standard controlled terminology exists, it should be used; otherwise sponsors should
define their own controlled list of terms. If the verbatim topic variable in an Interventions or Event domain is
modified to facilitate coding, the modified text is stored in --MODIFY. In most cases (other than PE), the dictionarycoded text is derived into --DECOD. Since the PEORRES variable is modified instead of the topic variable for PE,
the dictionary-derived text would be placed in PESTRESC. The variables used in each of the defined domains are:
Domain
AE
DS
CM
MH
PE

Original Verbatim
AETERM
DSTERM
CMTRT
MHTERM
PEORRES

Modified Verbatim
AEMODIFY
CMMODIFY
MHMODIFY
PEMODIFY

Standardized Value
AEDECOD
DSDECOD
CMDECOD
MHDECOD
PESTRESC

4.1.3.7 USE OF “YES” AND “NO” VALUES
Variables where the response is ―Yes‖ or ―No‖ (―Y‖ or ―N‖) should normally be populated for both ―Y‖ and ―N‖
responses. This eliminates confusion regarding whether a blank response indicates ―N‖ or is a missing value.
However, some variables are collected or derived in a manner that allows only one response, such as when a single
check box indicates ―Yes‖. In situations such as these, where it is unambiguous to only populate the response of
interest, it is permissible to only populate one value (―Y‖ or ―N‖) and leave the alternate value blank. An example of
when it would be acceptable to use only a value of ―Y‖ would be for Baseline Flag (--BLFL) variables, where ―N‖ is
not necessary to indicate that a value is not a baseline value.
Note: Permissible values for variables with controlled terms of ―Y‖ or ―N‖ may be extended to include ―U‖ or ―NA‖ if
it is the sponsor‘s practice to explicitly collect or derive values indicating ―Unknown‖ or ―Not Applicable‖ for that
variable.

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4.1.4 ACTUAL AND RELATIVE TIME ASSUMPTIONS
Timing variables (Table 2.2.5 of the SDTM) are an essential component of all SDTM subject-level domain datasets.
In general, all domains based on the three general observation classes should have at least one Timing variable. In
the Events or Interventions general observation class this could be the start date of the event or intervention. In the
Findings observation class where data are usually collected at multiple visits, at least one Timing variable must be
used.
The SDTMIG requires dates and times of day to be stored according to the international standard ISO 8601
(http://www.iso.org). ISO 8601 provides a text-based representation of dates and/or times, intervals of time, and
durations of time.
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4.1.4.1 FORMATS FOR DATE/TIME VARIABLES
An SDTM DTC variable may include data that is represented in ISO 8601 format as a complete date/time, a partial
date/time, or an incomplete date/time.
The SDTMIG template uses ISO 8601 for calendar dates and times of day, which are expressed as follows:
ο YYYY-MM-DDThh:mm:ss
where:
ο [YYYY] = four-digit year
ο [MM] = two-digit representation of the month (01-12, 01=January, etc.)
ο [DD] = two-digit day of the month (01 through 31)
ο [T] = (time designator) indicates time information follows
ο [hh] = two digits of hour (00 through 23) (am/pm is NOT allowed)
ο [mm] = two digits of minute (00 through 59)
ο [ss] = two digits of second (00 through 59)
Other characters defined for use within the ISO 8601 standard are:
ο [-] (hyphen): to separate the time Elements "year" from "month" and "month" from "day" and to represent
missing date components.
ο [:] (colon): to separate the time Elements "hour" from "minute" and "minute" from "second"
ο [/] (solidus): to separate components in the representation of date/time intervals
ο [P] (duration designator): precedes the components that represent the duration
ο NOTE: Spaces are not allowed in any ISO 8601 representations
Key aspects of the ISO 8601 standard are as follows:
•
•
•
•

ISO 8601 represents dates as a text string using the notation YYYY-MM-DD.
ISO 8601 represents times as a text string using the notation hh:mm:ss.
The SDTM and SDTMIG require use of the ISO 8601 Extended format, which requires hyphen delimiters
for date components and colon delimiters for time components. The ISO 8601 basic format, which does not
require delimiters, should not be used in SDTM datasets.
When a date is stored with a time in the same variable (as a date/time), the date is written in front of the
time and the time is preceded with “T” using the notation YYYY-MM-DDThh:mm:ss
(e.g. 2001-12-26T00:00:01).

Implementation of the ISO 8601 standard means that date/time variables are character/text data types. The SDS
fragment employed for date/time character variables is DTC.

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4.1.4.2 DATE/TIME PRECISION
The concept of representing date/time precision is handled through use of the ISO 8601 standard. According to ISO
8601, precision (also referred to by ISO 8601 as "completeness" or "representations with reduced accuracy") can be
inferred from the presence or absence of components in the date and/or time values. Missing components are
represented by right truncation or a hyphen (for intermediate components that are missing). If the date and time
values are completely missing the SDTM date field should be null. Every component except year is represented as
two digits. Years are represented as four digits; for all other components, one-digit numbers are always padded with
a leading zero.
The table below provides examples of ISO 8601 representation complete date and truncated date/time values using
ISO 8601 "appropriate right truncations" of incomplete date/time representations. Note that if no time component is
represented, the [T] time designator (in addition to the missing time) must be omitted in ISO 8601 representation.

1
2
3
4
5
6

Date and Time as
Originally Recorded
December 15, 2003 13:14:17
December 15, 2003 13:14
December 15, 2003 13
December 15, 2003
December, 2003
2003

Precision

ISO 8601 Date/Time

Complete date/time
Unknown seconds
Unknown minutes and seconds
Unknown time
Unknown day and time
Unknown month, day, and time

2003-12-15T13:14:17
2003-12-15T13:14
2003-12-15T13
2003-12-15
2003-12
2003

This date and date/time model also provides for imprecise or estimated dates, such as those commonly seen in
Medical History. To represent these intervals while applying the ISO 8601 standard, it is recommended that the
sponsor concatenate the date/time values (using the most complete representation of the date/time known) that
describe the beginning and the end of the interval of uncertainty and separate them with a solidus as shown in the
table below:

1
2
3
4

Interval of Uncertainty
Between 10:00 and 10:30 on the Morning of December 15, 2003
Between the first of this year (2003) until "now" (February 15, 2003)
Between the first and the tenth of December, 2003
Sometime in the first half of 2003

ISO 8601 Date/Time
2003-12-15T10:00/2003-12-15T10:30
2003-01-01/2003-02-15
2003-12-01/2003-12-10
2003-01-01/2003-06-30

Other uncertainty intervals may be represented by the omission of components of the date when these components
are unknown or missing. As mentioned above, ISO 8601 represents missing intermediate components through the
use of a hyphen where the missing component would normally be represented. This may be used in addition to
"appropriate right truncations" for incomplete date/time representations. When components are omitted, the
expected delimiters must still be kept in place and only a single hyphen is to be used to indicate an omitted
component. Examples of this method of omitted component representation are shown in the table below:

1
2
3
4
5
6

Date and Time as Originally
Recorded
December 15, 2003 13:15:17
December 15, 2003 ??:15
December 15, 2003 13:??:17
The 15th of some month in 2003, time
not collected
December 15, but can't remember the
year, time not collected
7:15 of some unknown date

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Level of Uncertainty

ISO 8601 Date/Time

Complete date
Unknown hour with known minutes
Unknown minutes with known date, hours,
and seconds
Unknown month and time with known year
and day
Unknown year with known month and day

2003-12-15T13:15:17
2003-12-15T-:15
2003-12-15T13:-:17

Unknown date with known hour and
minute

-----T07:15

2003---15
--12-15

CDISC SDTM Implementation Guide (Version 3.1.2)
Note that Row 6 above where a time is reported with no date information represents a very unusual situation. Since
most data is collected as part of a visit, when only a time appears on a CRF, it is expected that the date of the visit
would usually be used as the date of collection.
Using a character-based data type to implement the ISO 8601 date/time standard will ensure that the date/time
information will be machine and human readable without the need for further manipulation, and will be platform
and software independent.
4.1.4.3 INTERVALS OF TIME AND USE OF DURATION FOR --DUR VARIABLES
4.1.4.3.1 INTERVALS OF TIME AND USE OF DURATION FOR --DUR VARIABLES
As defined by ISO 8601, an interval of time is the part of a time axis, limited by two time "instants" such as the
times represented in SDTM by the variables --STDTC and --ENDTC. These variables represent the two instants that
bound an interval of time, while the duration is the quantity of time that is equal to the difference between these time
points.
ISO 8601 allows an interval to be represented in multiple ways. One representation, shown below, uses two dates in
the format:
YYYY-MM-DDThh:mm:ss/YYYY-MM-DDThh:mm:ss
While the above would represent the interval (by providing the start date/time and end date/time to "bound" the
interval of time), it does not provide the value of the duration (the quantity of time).
Duration is frequently used during a review; however, the duration timing variable (--DUR) should generally be
used in a domain if it was collected in lieu of a start date/time (--STDTC) and end date/time (--ENDTC). If both
--STDTC and --ENDTC are collected, durations can be calculated by the difference in these two values, and need
not be in the submission dataset.
Both duration and duration units can be provided in the single --DUR variable, in accordance with the ISO 8601
standard. The values provided in --DUR should follow one of the following ISO 8601 duration formats:
PnYnMnDTnHnMnS or PnW
where:





[P] (duration designator): precedes the alphanumeric text string that represents the duration. NOTE: The
use of the character P is based on the historical use of the term "period" for duration.
[n] represents a positive -number or zero
[W] is used as week designator, preceding a data Element that represents the number of calendar weeks
within the calendar year (e.g., P6W represents 6 weeks of calendar time).

The letter "P" must precede other values in the ISO 8601 representation of duration. The ―n‖ preceding each letter
represents the number of Years, Months, Days, Hours, Minutes, Seconds, or the number of Weeks. As with the
date/time format, ―T‖ is used to separate the date components from time components.
Note that weeks cannot be mixed with any other date/time components such as days or months in duration expressions.
As is the case with the date/time representation in --DTC, --STDTC, or --ENDTC only the components of duration
that are known or collected need to be represented. Also, as is the case with the date/time representation, if no time
component is represented, the [T] time designator (in addition to the missing time) must be omitted in ISO 8601
representation.
ISO 8601 also allows that the "lowest-order components" of duration being represented may be represented in
decimal format. This may be useful if data are collected in formats such as "one and one-half years", "two and onehalf weeks", "one-half a week" or "one quarter of an hour" and the sponsor wishes to represent this "precision" (or

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lack of precision) in ISO 8601 representation. Remember that this is ONLY allowed in the lowest-order (right-most)
component in any duration representation.
The table below provides some examples of ISO-8601-compliant representations of durations:
Duration as originally recorded
2 Years
10 weeks
3 Months 14 days
3 Days
6 Months 17 Days 3 Hours
14 Days 7 Hours 57 Minutes
42 Minutes 18 Seconds
One-half hour
5 Days 12¼ Hours
4 ½ Weeks

ISO 8601 Duration
P2Y
P10W
P3M14D
P3D
P6M17DT3H
P14DT7H57M
PT42M18S
PT0.5H
P5DT12.25H
P4.5W

Note that a leading zero is required with decimal values less than one.
4.1.4.3.2 INTERVAL WITH UNCERTAINTY
When an interval of time is an amount of time (duration) following an event whose start date/time is recorded (with
some level of precision, i.e. when one knows the start date/time and the duration following the start date/time), the
correct ISO 8601 usage to represent this interval is as follows:
YYYY-MM-DDThh:mm:ss/PnYnMnDTnHnMnS
where the start date/time is represented before the solidus [/], the "Pn…", following the solidus, represents a
―duration‖, and the entire representation is known as an ―interval‖. NOTE: This is the recommended representation
of elapsed time, given a start date/time and the duration elapsed.
When an interval of time is an amount of time (duration) measured prior to an event whose start date/time is
recorded (with some level of precision, i.e. where one knows the end date/time and the duration preceding that end
date/time), the syntax is:
PnYnMnDTnHnMnS/YYYY-MM-DDThh:mm:ss
where the duration, "Pn…", is represented before the solidus [/], the end date/time is represented following the
solidus, and the entire representation is known as an ―interval‖.
4.1.4.4 USE OF THE “STUDY DAY” VARIABLES
The permissible Study Day variables (--DY, --STDY, and --ENDY) describe the relative day of the observation
starting with the reference date as Day 1. They are determined by comparing the date portion of the respective
date/time variables (--DTC, --STDTC, and --ENDTC) to the date portion of the Subject Reference Start Date
(RFSTDTC from the Demographics domain).
The Subject Reference Start Date (RFSTDTC) is designated as Study Day 1. The Study Day value is incremented by
1 for each date following RFSTDTC. Dates prior to RFSTDTC are decremented by 1, with the date preceding
RFSTDTC designated as Study Day -1 (there is no Study Day 0). This algorithm for determining Study Day is
consistent with how people typically describe sequential days relative to a fixed reference point, but creates
problems if used for mathematical calculations because it does not allow for a Day 0. As such, Study Day is not
suited for use in subsequent numerical computations, such as calculating duration. The raw date values should be
used rather than Study Day in those calculations.

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All Study Day values are integers. Thus, to calculate Study Day:
--DY = (date portion of --DTC) - (date portion of RFSTDTC) + 1 if --DTC is on or after RFSTDTC
--DY = (date portion of --DTC) - (date portion of RFSTDTC) if --DTC precedes RFSTDTC
This algorithm should be used across all domains.
4.1.4.5 CLINICAL ENCOUNTERS AND VISITS
All domains based on the three general observation classes should have at least one timing variable. For domains in
the Events or Interventions observations classes, and for domains in the Findings observation class for which data
are collected only once during the study, the most appropriate timing variable may be a date (e.g., --DTC, --STDTC)
or some other timing variable. For studies that are designed with a prospectively defined schedule of visit-based
activities, domains for data that are to be collected more than once per subject (e.g., Labs, ECG, Vital Signs) are
expected to include VISITNUM as a timing variable.
Clinical encounters are described by the CDISC Visit variables. For planned visits, values of VISIT, VISITNUM,
and VISITDY must be those defined in the Trial Visits dataset, see Section 7.4. For planned visits:
 Values of VISITNUM are used for sorting and should, wherever possible, match the planned chronological
order of visits. Occasionally, a protocol will define a planned visit whose timing is unpredictable (e.g., one
planned in response to an adverse event, a threshold test value, or a disease event), and completely
chronological values of VISITNUM may not be possible in such a case.
 There should be a one-to-one relationship between values of VISIT and VISITNUM.
 For visits that may last more than one calendar day, VISITDY should be the planned day of the start of the visit.
34H

Sponsor practices for populating visit variables for unplanned visits may vary across sponsors.
 VISITNUM should generally be populated, even for unplanned visits, as it is expected in many Findings
domains, as described above. The easiest method of populating VISITNUM for unplanned visits is to assign
the same value (e.g., 99) to all unplanned visits, but this method provides no differentiation between the
unplanned visits and does not provide chronological sorting. Methods that provide a one-to-one relationship
between visits and values of VISITNUM, that are consistent across domains, and that assign VISITNUM
values that sort chronologically require more work and must be applied after all of a subject's unplanned visits
are known.
 VISIT may be left null or may be populated with a generic value (e.g., "Unscheduled") for all unplanned
visits, or individual values may be assigned to different unplanned visits.
 VISITDY should not be populated for unplanned visits, since VISITDY is, by definition, the planned study
day of visit, and since the actual study day of an unplanned visit belongs in a --DY variable.
The following table shows an example of how the visit identifiers might be used for lab data:
USUBJID
001
001
001

VISIT
Week 1
Week 2
Week 2 Unscheduled

VISITNUM
2
3
3.1

VISITDY
7
14

LBDY
7
13
17

4.1.4.6 REPRESENTING ADDITIONAL STUDY DAYS
The SDTM allows for --DTC values to be represented as study days (--DY) relative to the RFSTDTC reference start
date variable in the DM dataset, as described above in Section 4.1.4.4. The calculation of additional study days
within subdivisions of time in a clinical trial may be based on one or more sponsor-defined reference dates not
represented by RFSTDTC. In such cases, the Sponsor may define Supplemental Qualifier variables and the
define.xml should reflect the reference dates used to calculate such study days. If the sponsor wishes to define ―day
within element‖ or ―day within epoch,‖ the reference date/time will be an element start date/time in the Subject
Elements dataset (Section 5.3.1).
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4.1.4.7 USE OF RELATIVE TIMING VARIABLES
--STRF and --ENRF
The variables --STRF and --ENRF represent the timing of an observation relative to the sponsor-defined reference period
when information such as "BEFORE‖, ―PRIOR‖,‖ONGOING‖', or ―CONTINUING‖ is collected in lieu of a date and this
collected information is in relation to the sponsor-defined reference period. The sponsor-defined reference period is the
continuous period of time defined by the discrete starting point (RFSTDTC) and the discrete ending point (RFENDTC) for
each subject in the Demographics dataset.
--STRF is used to identify the start of an observation relative to the sponsor-defined reference period.
--ENRF is used to identify the end of an observation relative to the sponsor-defined reference period.
Allowable values for --STRF and --ENRF are ―BEFORE‖, ―DURING‖, ―DURING/AFTER‖, ―AFTER‖, and ―U‖ (for
unknown).
As an example, a CRF checkbox that identifies concomitant medication use that began prior to the study treatment
period would translate into CMSTRF = ―BEFORE‖ if selected. Note that in this example, the information collected
is with respect to the start of the concomitant medication use only and therefore the collected data corresponds to
variable CMSTRF, not CMENRF. Note also that the information collected is relative to the study treatment period,
which meets the definition of CMSTRF.
Some sponsors may wish to derive --STRF and --ENRF for analysis or reporting purposes even when dates are
collected. Sponsors are cautioned that doing so in conjunction with directly collecting or mapping data such as
―BEFORE‖, ―PRIOR", etc. to --STRF and --ENRF will blur the distinction between collected and derived values
within the domain. Sponsors wishing to do such derivations are instead encouraged to use supplemental variables or
analysis datasets for this derived data.
In general, sponsors are cautioned that representing information using variables --STRF and --ENRF may not be as
precise as other methods, particularly because information is often collected relative to a point in time or to a period
of time other than the one defined as the study reference period. SDTMIG V3.1.2 has attempted to address these
limitations by the addition of four new relative timing variables, which are described in the following paragraph.
Sponsors should use the set of variables that allows for accurate representation of the collected data. In many cases,
this will mean using these new relative timing variables in place of --STRF and --ENRF.
--STRTPT, --STTPT, --ENRTPT, and --ENTPT
While the variables --STRF and --ENRF are useful in the case when relative timing assessments are made coincident
with the start and end of the study reference period, these may not be suitable for expressing relative timing
assessments such as ―Prior‖ or ―Ongoing‖ that are collected at other times of the study. As a result, four new timing
variables have been added in V3.1.2 to express a similar concept at any point in time. The variables --STRTPT and
--ENRTPT contain values similar to --STRF and --ENRF, but may be anchored with any timing description or
date/time value expressed in the respective --STTPT and --ENTPT variables, and not be limited to the study reference
period. Unlike the variables --STRF and --ENRF, which for all domains are defined relative to one study reference
period, the timing variables --STRTPT, --STTPT, --ENRTPT, and --ENTPT are defined to be unique within a domain
only. Allowable values for --STRTPT and --ENRTPT are as follows:
If the reference time point corresponds to the date of collection or assessment:
 Start values: an observation can start BEFORE that time point, can start COINCIDENT with that time
point, or it is unknown (U) when it started
 End values: an observation can end BEFORE that time point, can end COINCIDENT with that time point, can
be known that it didn‘t end but was ONGOING, or it is unknown (U) at all when it ended or if it was ongoing.
 AFTER is not a valid value in this case because it would represent an event after the date of collection.
If the reference time point is prior to the date of collection or assessment:
 Start values: an observation can start BEFORE the reference point, can start COINCIDENT with the
reference point, can start AFTER the reference point, or it may not be known (U) when it started
 End values: an observation can end BEFORE the reference point, can end COINCIDENT with the
reference point, can end AFTER the reference point, can be known that it didn‘t end but was ONGOING, or
it is unknown (U) when it ended or if it was ongoing.
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Examples of --STRTPT, --STTPT, --ENRTPT, and --ENTPT
1.

Medical History
Assumptions:
 CRF contains "Year Started" and check box for "Active"
 "Date of Assessment" is collected
Example when "Active" is checked:
MHDTC = date of assessment value, ex. "2006-11-02"
MHSTDTC = year of condition start, e.g., "2002"
MHENRTPT = "ONGOING"
MHENTPT = date of assessment value, e.g., "2006-11-02"

Figure 4.1.4.7 Example of --ENRTPT and --ENTPT for Medical History

2002

MHENTPT
Assessment Date and
Reference Time Point of
2006-11-02

MHDTC = 2006-11-02
MHSTDTC = 2002
MHENRTPT = ONGOING
MHENTPT = 2006-11-02
Medical event began in 2002 and
was ongoing at the reference time
point of 2006-11-02. The medical
event may or may not have ended
at any time after that.

Prior and Concomitant Medications
Assumptions:
 CRF contains "Start Date", "Stop Date", and check boxes for "Prior" if unknown or uncollected Start Date, and
"Continuing" if no Stop Date was collected. Prior refers to screening visit and Continuing refers to final study visit.
Example when both "Prior" and "Continuing" are checked:
CMSTDTC = [null]
CMENDTC = [null]
CMSTRTPT = "BEFORE"
CMSTTPT = screening date, e.g., "2006-10-21"
CMENRTPT = "ONGOING"
CMENTPT = final study visit date, e.g., "2006-11-02"
2.

Adverse Events
Assumptions:
 CRF contains "Start Date", "Stop Date", and "Outcome" with check boxes including "Continuing" and
"Unknown" (Continuing and Unknown are asked at the end of the subject's study participation)
 No assessment date or visit information is collected
Example when "Unknown" is checked:
AESTDTC = start date, e.g., "2006-10-01"
AEENDTC = [null]
AEENRTPT = "U"
AEENTPT = final subject contact date, e.g., "2006-11-02"

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4.1.4.8 DATE AND TIME REPORTED IN A DOMAIN BASED ON FINDINGS
When the date/time of collection is reported in any domain, the date/time should go into the --DTC field (e.g., EGDTC for
Date/Time of ECG). For any domain based on the Findings general observation class, such as lab tests which are based on a
specimen, the collection date is likely to be tied to when the specimen or source of the finding was captured, not necessarily
when the data was recorded. In order to ensure that the critical timing information is always represented in the same variable,
the --DTC variable is used to represent the time of specimen collection. For example, in the LB domain the LBDTC variable
would be used for all single-point blood collections or spot urine collections. For timed lab collections (e.g., 24-hour urine
collections) the LBDTC variable would be used for the start date/time of the collection and LBENDTC for the end date/time
of the collection. This approach will allow the single-point and interval collections to use the same date/time variables
consistently across all datasets for the Findings general observation class. The table below illustrates the proper use of these
variables. Note that --STDTC is not used for collection dates over an interval, so is blank in the following table.
Collection Type
Single-Point Collection
Interval Collection

--DTC
X
X

--STDTC

--ENDTC
X

4.1.4.9 USE OF DATES AS RESULT VARIABLES
Dates are generally used only as timing variables to describe the timing of an event, intervention, or collection activity, but there may
be occasions when it may be preferable to model a date as a result (--ORRES) in a Findings dataset. Note that using a date as a result
to a Findings question is unusual and atypical, and should be approached with caution, but this situation may occasionally occur when
a) a group of questions (each of which has a date response) is asked and analyzed together; or b) the Event(s) and Intervention(s) in
question are not medically significant (often the case when included in questionnaires). Consider the following cases:




Calculated due date
Date of last day on the job
Date of high school graduation

One approach to modeling these data would be to place the text of the question in --TEST and the response to the
question, a date represented in ISO 8601 format, in --ORRES and --STRESC as long as these date results do not
contain the dates of medically significant events or interventions.
Again, use extreme caution when storing dates as the results of Findings. Remember, in most cases, these dates
should be timing variables associated with a record in an Intervention or Events dataset.
4.1.4.10

REPRESENTING TIME POINTS

Time points can be represented using the time point variables, --TPT, --TPTNUM, --ELTM, and the time point
anchors, --TPTREF (text description) and --RFTDTC (the date/time). Note that time-point data will usually have an
associated --DTC value. The interrelationship of these variables is shown in Figure 4.1.4.10 below.
Figure 4.1.4.10

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Values for these variables for Vital Signs measurements taken at 30, 60, and 90 minutes after dosing would look like
the following.
VSTPTNUM VSTPT VSELTM
VSTPTREF
VSRFTDTC
VSDTC
1
30 MIN PT30M
DOSE ADMINISTRATION
2006-08-01T08:00
2006-08-01T08:30
2
60 MIN PT1H
DOSE ADMINISTRATION
2006-08-01T08:00
2006-08-01T09:01
3
90 MIN PT1H30M DOSE ADMINISTRATION
2006-08-01T08:00
2006-08-01T09:32
Note that the actual elapsed time is not an SDTM variable, but can be derived by an algorithm representing VSDTC-VSRFTDTC.

Values for these variables for Urine Collections taken pre-dose, and from 0-12 hours and 12-24 hours after dosing
would look like the following.
LBTPTNUM
LBTPT
LBELTM
LBTPTREF
LBRFTDTC
1
15 MIN PRE-DOSE
-PT15M
DOSE ADMINISTRATION 2006-08-01T08:00
2
0-12 HOURS
PT12H
DOSE ADMINISTRATION 2006-08-01T08:00
3
12-24 HOURS
PT24H
DOSE ADMINISTRATION 2006-08-01T08:00
Note that the value in LBELTM represents the end of the interval at which the collection ends.

LBDTC
2006-08-01T08:30
2006-08-01T20:35
2006-08-02T08:40

When time points are used, --TPTNUM is expected. Time points may or may not have an associated --TPTREF.
Sometimes, --TPTNUM may be used as a key for multiple values collected for the same test within a visit; as such,
there is no dependence upon an anchor such as --TPTREF, but there will be a dependency upon the VISITNUM. In
such cases, VISITNUM will be required to confer uniqueness to values of --TPTNUM.
If the protocol describes the scheduling of a dose using a reference intervention or assessment, then --TPTREF
should be populated, even if it does not contribute to uniqueness. The fact that time points are related to a reference
time point, and what that reference time point is, are important for interpreting the data collected at the time point.
Not all time points will require all three variables to provide uniqueness. In fact, in some cases a time point may be
uniquely identified without the use of VISIT, or without the use of --TPTREF, or, rarely, without the use of either
one. For instance:
 A trial might have time points only within one visit, so that the contribution of VISITNUM to uniqueness is
trivial.
 A trial might have time points that do not relate to any visit, such as time points relative to a dose of drug
self-administered by the subject at home.
 A trial may have only one reference time point per visit, and all reference time points may be similar, so that
only one value of --TPTREF (e.g., "DOSE") is needed.
 A trial may have time points not related to a reference time point. For instance, --TPTNUM values could be
used to distinguish first, second, and third repeats of a measurement scheduled without any relationship to
dosing.
For trials with many time points, the requirement to provide uniqueness using only VISITNUM, --TPTREF, and
--TPTNUM may lead to a scheme where multiple natural keys are combined into the values of one of these variables.
For instance, in a crossover trial with multiple doses on multiple days within each period, either of the following
options could be used. VISITNUM might be used to designate period, --TPTREF might be used to designate the day
and the dose, and --TPTNUM might be used to designate the timing relative to the reference time point. Alternatively,
VISITNUM might be used to designate period and day within period, --TPTREF might be used to designate the dose
within the day, and --TPTNUM might be used to designate the timing relative to the reference time point.

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Option 1
VISIT
PERIOD 1

PERIOD 2

VISITNUM
3

--TPT
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H

--TPTNUM
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3

--TPTREF
DAY 1, AM DOSE

DAY 1, PM DOSE

DAY 5, AM DOSE

DAY 5, PM DOSE

DAY 1, AM DOSE

DAY 1, PM DOSE

Option 2
VISIT
PERIOD 1, DAY 1

PERIOD 1, DAY 5

PERIOD 2, DAY 1

VISITNUM
3

--TPT
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H
PRE-DOSE
1H
4H

--TPTNUM
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3

--TPTREF
AM DOSE

PM DOSE

AM DOSE

PM DOSE

AM DOSE

PM DOSE

Within the context that defines uniqueness for a time point, which may include domain, visit, and reference time
point, there must be a one-to-relationship between values of --TPT and --TPTNUM. In other words, if domain, visit,
and reference time point uniquely identify subject data, then if two subjects have records with the same values of
DOMAIN, VISITNUM, --TPTREF, and --TPTNUM, then these records may not have different time point
descriptions in --TPT.
Within the context that defines uniqueness for a time point, there is likely to be a one-to-one relationship between
most values of --TPT and --ELTM. However, since --ELTM can only be populated with ISO 8601 periods of time
(as described in Section 4.1.4.3), --ELTM may not be populated for all time points. For example, --ELTM is likely to
be null for time points described by text such as "pre-dose" or "before breakfast." When --ELTM is populated, if two
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subjects have records with the same values of DOMAIN, VISITNUM, --TPTREF, and --TPTNUM, then these
records may not have different values in --ELTM.
When the protocol describes a time point with text such as "4-6 hours after dose" or "12 hours +/- 2 hours after
dose" the sponsor may choose whether and how to populate --ELTM. For example, a time point described as "4-6
hours after dose" might be associated with an --ELTM value of PT4H. A time point described as "12 hours +/- 2
hours after dose" might be associated with an --ELTM value of PT12H. Conventions for populating --ELTM should
be consistent (the examples just given would probably not both be used in the same trial). It would be good practice
to indicate the range of intended timings by some convention in the values used to populate --TPT.
Sponsors may, of course, use more stringent requirements for populating --TPTNUM, --TPT, and --ELTM. For
instance, a sponsor could decide that all time points with a particular --ELTM value would have the same values of
--TPTNUM and --TPT, across all visits, reference time points, and domains.

4.1.5 OTHER ASSUMPTIONS
4.1.5.1 ORIGINAL AND STANDARDIZED RESULTS OF FINDINGS AND TESTS NOT DONE
4.1.5.1.1 ORIGINAL AND STANDARDIZED RESULTS
The --ORRES variable contains the result of the measurement or finding as originally received or collected.
--ORRES is an expected variable and should always be populated, with two exceptions:



When --STAT = ―NOT DONE‖
--ORRES should generally not be populated for derived records

Derived records are flagged with the --DRVFL variable. When the derived record comes from more than one visit,
the sponsor must define the value for VISITNUM, addressing the correct temporal sequence. If a new record is
derived for a dataset, and the source is not eDT, then that new record should be flagged as derived. For example in
ECG data, if QTc Intervals are derived in-house by the sponsor, then the derived flag is set to ―Y‖. If the QTc
Intervals are received from a vendor the derived flag is not populated.
When --ORRES is populated, --STRESC must also be populated, regardless of whether the data values are character
or numeric. The variable, --STRESC, is derived either by the conversion of values in --ORRES to values with
standard units, or by the assignment of the value of --ORRES (as in the PE Domain, where --STRESC could contain
a dictionary-derived term). A further step is necessary when --STRESC contains numeric values. These are
converted to numeric type and written to --STRESN. Because --STRESC may contain a mixture of numeric and
character values, --STRESN may contain null values, as shown in the flowchart below.
--ORRES
(all original values)

--STRESC
(derive or copy all results)

--STRESN
(numeric results only)

When the original measurement or finding is a selection from a defined codelist, in general, the --ORRES and
--STRESC variables contain results in decoded format, that is, the textual interpretation of whichever code was
selected from the codelist. In some cases where the code values in the codelist are statistically meaningful
standardized values or scores, which are defined by sponsors or by valid methodologies such as SF36
questionnaires, the --ORRES variables will contain the decoded format, whereas, the --STRESC variables as well as
the --STRESN variables will contain the standardized values or scores.
Occasionally data that are intended to be numeric are collected with characters attached that cause the character-to-numeric
conversion to fail. For example, numeric cell counts in the source data may be specified with a greater than (>) or less than (<)
sign attached (e.g. >10, 000 or <1). In these cases the value with the greater than (>) or less than (<) sign attached should be
moved to the --STRESC variable, and --STRESN should be null. The rules for modifying the value for analysis purposes
should be defined in the analysis plan and only changed in the ADaM datasets. If the value in --STRESC has different units,
the greater than (>) or less than (<) sign should be maintained. An example is included in Section 4.1.5.1.3, Rows 11 and 12.
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4.1.5.1.2 TESTS NOT DONE
When an entire examination (Laboratory draw, ECG, Vital Signs, or Physical Examination), or a group of tests
(hematology or urinalysis), or an individual test (glucose, PR interval, blood pressure, or hearing) is not done, and
this information is explicitly captured on the CRF with a yes/no or done/not done question, this information should
be presented in the dataset. The reason for the missing information may or may not have been collected. A sponsor
has two options; one is to submit individual records for each test not done or to submit one record for a group of
tests that were not done. See the examples below for submitting groups of tests not done.
If the data on the CRF is missing and yes/no or done/not done was not explicitly captured a record should not be
created to indicate that the data was not collected.
If a group of tests were not done:
 --TESTCD should be --ALL
 --TEST should be
 --CAT should be
 --ORRES should be null
 --STAT should be ―NOT DONE‖
 --REASND, if collected, might be ‖Specimen lost‖
For example, if urinalysis is not done then:
 LBTESTCD should be ―LBALL‖
 LBTEST should be ―Labs Data‖
 LBCAT should be "URINALYSIS"
 LBORRES should be NULL
 LBSTAT should be ―NOT DONE‖
 LBREASND, if collected, might be ―Subject could not void‖
4.1.5.1.3 EXAMPLES OF ORIGINAL AND STANDARD UNITS AND TEST NOT DONE
The following examples are meant to illustrate the use of Findings results variables, and are not meant as
comprehensive domain examples. Certain required and expected variables are omitted, and the samples may
represent data for more than one subject.
Lab Data Examples
 Numeric values that have been converted (Row 1) or copied (Row 3).
 A character result that has been copied (Row 2).
 A result of ―TRACE‖ shows ―TRACE‖ in LBSTRESC and LBSTRESN is null (Row 4).
 Value of 1+ in LBORRES, 1+ in LBSTRESC and LBSTRESN is null (Row 5).
 A result of ―BLQ‖ was collected. That value was copied to LBSTRESC and LBSTRESN is null. Note that
the standard units are populated by sponsor decision, but could be left null. (Row 6).
 A result is missing because the observation was ―NOT DONE‖, as reflected in the --STAT variable; neither
LBORRES nor LBSTRESC are populated (Row 7).
 A result is derived from multiple records such as an average of baseline measurements for a baseline value,
so LBDRVFL = Y (Row 8). Note that the original collected data are not shown in this example.
 None of the scheduled tests were completed as planned (Row 9).
 A category of tests was not completed as planned (Row 10).
 Shows when LBSTRESC has been standardized and the less than (<) sign has been maintained (Row 11).
 Shows when LBSTRESC has been standardized and the less than (<) sign has been maintained (Row 12).

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Row
1
2
3
4
5
6
7
8
9
10
11
12

LBTESTCD
GLUC
BACT
ALT
RBC
WBC
KETONES
HCT
MCHC
LBALL
LBALL
WBC
BILI

LBCAT
CHEMISTRY
URINALYSIS
CHEMISTRY
URINALYSIS
URINALYSIS
CHEMISTRY
HEMATOLOGY
HEMATOLOGY
HEMATOLOGY
HEMATOLOGY
CHEMISTRY

LBORRES
6.0
MODERATE
12.1
TRACE
1+
BLQ

LBORRESU
mg/dL
mg/L

mg/L

LBSTRESC
60.0
MODERATE
12.1
TRACE
1+
BLQ

LBSTRESN
60.0

LBSTRESU
mg/L

12.1

mg/L

33.8

LBSTAT

LBDRVFL

mg/L
NOT DONE
g/dL

Y
NOT DONE
NOT DONE

<4, 000
<0.1

/mm3
mg/dL

<4,000
<1.71

/mm3
umol/L

The SDS Team realizes that for rows 4, 5, and 6, this change is not backward compatible, but the example has been
modified to reflect harmonization with ADaM and comments received during the review period. The changes are
directed at decreasing the amount of sponsor subjectivity in converting original results to standard results.
ECG Examples:
 Numeric and character values that have been converted (Rows 2 and 3) or copied (Rows 1 and 4).
 A result is missing because the test was ―NOT DONE‖, as reflected in the EGSTAT variable; neither
EGORRES nor EGSTRESC is populated (Row 5).
 The overall interpretation is included as a new record (Row 6)
 The entire ECG was not done (Row 7)
Row
1
2
3
4
5
6
7

EGTESTCD
QRSDUR
QTMEAN
QTCB
RHYMRATE
PRMEAN
INTP
EGALL

EGORRES
0.362
221
412
ATRIAL FLUTTER

EGORRESU
sec
msec
msec

EGSTRESC
0.362
.221
.412
ATRIAL FLUTTER

EGSTRESN
0.362
.221
.412

EGSTRESU
sec
sec
sec

EGSTAT

EGDRVFL

NOT DONE
ABNORMAL

ABNORMAL
NOT DONE

Vital Signs Example:
 Numeric values that have converted (Rows 1 and 2).
 A result is missing because the Vital Signs test was ―NOT DONE‖, as reflected in the VSSTAT variable;
neither VSORRES nor VSSTRESC is populated (Row 3).
 The result is derived by having multiple records for one measurement (Rows 4 and 5), and the derived
value is recorded in a new row with the derived record flagged. (Row 6).
 The entire examination was not done (Row 7).
Row
1
2
3
4
5
6
7

VSTESTCD
HEIGHT
WEIGHT
HR
SYSBP
SYSBP
SYSBP
VSALL

VSORRES
60
110

VSORRESU
IN
LB

VSSTRESC
152
50

VSSTRESN
152
50

VSSTRESU
cm
kg

96
100

mmHg
mmHg

96
100
98

mmHg
mmHg
mmHg

VSSTAT

VSDRVFL

NOT DONE

Y
NOT DONE

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Questionnaire Example:
 Note that this is for a standard instrument for which no subjectivity is involved in representing the original result as
a numeric value.
 A Character value that has been converted to a standard score (Rows 1, 5, and 6).
 A result is derived from multiple records (Row 2). The records for the original collected results are not shown in
this example.
 A result is missing because the observation was ―NOT DONE‖, as reflected in the QSSTAT variable;
neither QSORRES nor QSSTRESC is populated (Row 3).
 The entire questionnaire was not done (Row 4).
 Shows when a summary score in Row 7 is derived from the data in Rows 5 and 6 and QSORRES should not
be populated because the character values cannot be added to give a meaningful result (Rows 5, 6, and 7).
Row
1
2
3
4
5
6
7

QSTESTCD
QS1
QS2
QS1
QSALL
QSP10
QSP11
QSPSUM

QSTEST
Health
Health Perceptions (0-100)
Health
Questionnaire
Healthy As Anyone
Expect Health To Get Better
Total of Scores

QSORRES
VERY GOOD

QSSTRESC
4.4
82

QSSTRESN
4.4
82

QSSTAT

QSDRVFL
Y

NOT DONE
NOT DONE
MOSTLY TRUE
DEFINITELY TRUE

4
5
9

4.1.5.2 LINKING OF MULTIPLE OBSERVATIONS
See Section 8 for guidance on expressing relationships among multiple observations.
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4.1.5.3 TEXT STRINGS THAT EXCEED THE MAXIMUM LENGTH FOR GENERALOBSERVATION-CLASS DOMAIN VARIABLES
4.1.5.3.1 TEST NAME (--TEST) GREATER THAN 40 CHARACTERS
Sponsors may have test descriptions (--TEST) longer than 40 characters in their operational database. Since the
--TEST variable is meant to serve as a label for a --TESTCD when a Findings dataset is transposed to a more
horizontal format, the length of --TEST is normally limited to 40 characters to conform to the limitations of the SAS
V5 Transport format currently used for submission datasets. Therefore, sponsors have the choice to either insert the
first 40 characters or a text string abbreviated to 40 characters in --TEST. Sponsors should include the full
description for these variables in the study metadata in one of two ways:


If the annotated CRF contains the full text, provide a link to the annotated CRF page containing the full test
description in the define.xml Origin column for --TEST.



If the annotated CRF does not specify the full text, then create a pdf document to store full-text descriptions.
In the define.xml Comments column for --TEST insert a link to the full test description in the pdf.

The convention above should also be applied to the Qualifier Value Label (QLABEL) in Supplemental Qualifiers
(SUPP--) datasets. IETEST values in IE and TI are exceptions to the above 40-character rule and are limited to 200
characters since they are not expected to be transformed to a column labels. Values of IETEST that exceed 200
characters should be described in study metadata as per the convention above. For further details see IE domain
Section 6.3.2.1 Assumption 4 and TI domain Section 7.5.2 Assumption 5.
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4.1.5.3.2 TEXT STRINGS> 200 CHARACTERS IN OTHER VARIABLES
Some sponsors may collect data values longer than 200 characters for some variables. Because of the current
requirement for Version 5 SAS transport file format, it will not be possible to store those long text strings using only
one variable. Therefore, the SDTMIG has defined a convention for storing a long text string by using a combination
of the standard domain dataset and the Supplemental Qualifiers (SUPP--) datasets, which applies to all domains
based on a general observation class. Note that the Comments domain is not based on a general observation class
and has different rules. See Section 5.2 for information on handling comment text more than 200 characters long.
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The first 200 characters of text should be stored in the standard domain variable and each additional 200 characters
of text should be stored as a record in the SUPP-- dataset (see Section 8.4). In this dataset, the value for QNAM
should contain a sequential variable name, which is formed by appending a one-digit integer, beginning with 1, to
the original standard domain variable name. When splitting a text string into several records, the text should be split
between words to improve readability.
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As an example, if there was a verbatim response for a Medical History Reported Term (MHTERM) of 500 characters
in length, the sponsor would put the first 200 characters of text in the standard domain variable and dataset (MHTERM
in MH), the next 200 characters of text as a first supplemental record in the SUPPMH dataset, and the final 100
characters of text as a second record in the SUPPMH dataset (see Example 1 below). Variable QNAM would have the
values MHTERM1 and MHTERM2 for these two records in SUPPMH, respectively, for this one particular text string.
Sponsors should place the text itself into variable QVAL and the label of the original standard domain variable into
variable QLABEL. In this case, IDVAR and IDVARVAL should be used in SUPPMH to relate the associated
supplemental text records to the parent record containing the first 200 characters of text in the standard domain.
In cases where the standard domain variable name is already 8 characters in length, sponsors should replace the last
character with a digit when creating values for QNAM. As an example, for Other Action Taken in Adverse Events
(AEACNOTH), values for QNAM for the SUPPAE records would have the values AEACNOT1, AEACNOT2, and so on.
Example 1: MHTERM with 500 characters.
suppmh.xpt
STUDYID RDOMAIN USUBJID IDVAR IDVARVAL
12345
MH
99-123
MHSEQ
6

12345

99-123

MHSEQ

QNAM
MHTERM1

MHTERM2

QLABEL
QVAL
QORIG QEVAL
Reported Term
2nd 200
CRF
for the Medical chars of text
History
Reported Term
last 100
CRF
for the Medical chars of text
History

Example 2: AEACN with 400 characters.
suppae.xpt
STUDYID RDOMAIN USUBJID
12345
AE
99-123

IDVAR IDVARVAL
AESEQ
4

QNAM
AEACNOT1

QLABEL
Other Action
Taken

QVAL
QORIG QEVAL
2nd 200
CRF
chars of text

The only exceptions to the above rules are Comments (CO) and TS (Trial Summary). Please see section 5.2.1.1 for
Comments and Section 7.6.1 for Trial Summary. NOTE: Only the Comments (CO) and Trial Summary (TS)
domains are allowed to add variables for the purpose of handling text exceeding 200 characters. All other domains
must use SUPPQUAL variables as noted in the examples above.
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4.1.5.4 EVALUATORS IN THE INTERVENTIONS AND EVENTS OBSERVATION CLASSES
The observations recorded in the Findings class include the --EVAL qualifier because the observation may originate
from more than one source (e.g., an Investigator or Central Reviewer). For the Interventions and Events observation
classes, which do not include the --EVAL variable, all data are assumed to be attributed to the Principal Investigator.
The QEVAL variable can be used to describe the evaluator for any data item in a SUPP-- dataset (Section 8.4.1), but
is not required when the data are objective. For observations that have primary and supplemental evaluations of
specific qualifier variables, sponsors should put data from the primary evaluation into the standard domain dataset
and data from the supplemental evaluation into the Supplemental Qualifier datasets (SUPP--). Within each SUPP-record, the value for QNAM should be formed by appending a ―1‖ to the corresponding standard domain variable
name. In cases where the standard domain variable name is already eight characters in length, sponsors should
replace the last character with a ―1‖ (incremented for each additional attribution). The following is an example of
how to represent the case where an adjudication committee evaluates an adverse event in SUPPAE. See Section 8.4
for additional details on how to use SUPP--.
35H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Note that QNAM takes on the value AERELNS1, as the corresponding standard domain variable AERELNST is
already eight characters in length. The adverse event data as determined by the primary investigator would reside in
the standard AE dataset.

STUDYID RDOMAIN USUBJID IDVAR IDVARVAL

QNAM

12345

99-123

AESEQ

AESEV1

12345

99-123

AESEQ

AEREL1

12345

99-123

AESEQ

QLABEL
Severity/
Intensity
Causality

AERELNS1 Relationship
to Non-Study
Treatment

QVAL

QORIG

MILD

CRF

POSSIBLY
RELATED
Possibly
related to
aspirin use

CRF
CRF

QEVAL
ADJUDICATION
COMMITTEE
ADJUDICATION
COMMITTEE
ADJUDICATION
COMMITTEE

4.1.5.5 CLINICAL SIGNIFICANCE FOR FINDINGS OBSERVATION CLASS DATA
For assessments of clinical significance when the overall interpretation is a record in the domain, use Supplemental
Qualifier (SUPP--) record (with QNAM = --CLSIG) linked to the record that contains the overall interpretation or a
particular result. An example would be a QNAM value of EGCLSIG in SUPPEG with a value of ―Y‖, indicating
that an ECG result of ATRIAL FIBRILLATION was clinically significant.
Separate from clinical significance are results of NORMAL or ABNORMAL, or lab values which are out of range.
Examples of the latter include the following:
 An ECG test with EGTESTCD=INTP addresses the ECG as a whole should have a result or of NORMAL
or ABNORMAL. A record for EGTESTCD=INTP may also have a record in SUPPEG indicating whether
the result is clinically significant.
 A record for a vital signs measurement (e.g., systolic blood pressure) or a lab test (e.g., hematocrit) that
contains a measurement may have a normal range and a normal range indicator. It could also have a
SUPP-- record indicating whether the result was clinically significant.
4.1.5.6 SUPPLEMENTAL REASON VARIABLES
The SDTM general observation classes include the --REASND variable to submit the reason an observation was not
collected. However, sponsors sometimes collect the reason that something was done. For the Interventions general
observation class, --INDC and --ADJ are available to indicate the reason for the intervention or for the dose adjustment.
For the Findings general observation class, if the sponsor collects the reason for performing a test or examination, it
should be placed in the SUPP-- dataset as described in Section 8.4.1. The standard SUPP-- QNAM value of --REAS
should be used as described in Appendix C5. If multiple reasons are reported, refer to Section 4.1.2.8.3.
357H

1867H

358H

For example, if the sponsor collects the reason that extra lab tests were done, the SUPP-- record might be populated
as follows:
STUDYID RDOMAIN USUBJID

12345

99-123

IDVAR

LBSEQ

IDVARVAL

QNAM

QLABEL

LBREAS Reason Test or Examination
was Performed

QVAL

ORIGINAL
SAMPLE LOST

QORIG

CRF

4.1.5.7 PRESENCE OR ABSENCE OF PRE-SPECIFIED INTERVENTIONS AND EVENTS
Interventions (e.g., concomitant medications) and Events (e.g., medical history) can generally be collected in two
different ways, by recording either verbatim free text or the responses to a pre-specified list of treatments or terms.
Since the method of solicitation for information on treatments and terms may affect the frequency at which they are
reported, whether they were pre-specified may be of interest to reviewers. The --PRESP variable is used to indicate
whether a specific intervention (--TRT) or event (--TERM) was solicited. The --PRESP variable has controlled
terminology of Y (for ―Yes‖) or a null value. It is a permissible variable, and should only be used when the topic
variable values come from a pre-specified list. Questions such as ―Did the subject have any concomitant
medications?‖ or ―Did the subject have any medical history?‖ should not have records in SDTM domain because 1)

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CDISC SDTM Implementation Guide (Version 3.1.2)
these are not valid values for the respective topic variables of CMTRT and MHTERM, and 2) records whose sole
purpose is to indicate whether or not a subject had records are not meaningful.
The --OCCUR variable is used to indicate whether a pre-specified intervention or event occurred or did not occur. It
has controlled terminology of Y and N (for ―Yes‖ and ―No‖). It is a permissible variable and may be omitted from
the dataset if no topic-variable values were pre-specified.
If a study collects both pre-specified interventions and events as well as free-text events and interventions, the value
of --OCCUR should be ―Y‖ or ―N‖ for all pre-specified interventions and events, and null for those reported as freetext.
The --STAT and --REASND variables can be used to provide information about pre-specified interventions and
events for which there is no response (e.g., investigator forgot to ask). As in Findings, --STAT has controlled
terminology of NOT DONE.
Situation
Spontaneously reported event occurred
Pre-specified event occurred
Pre-specified event did not occur
Pre-specified event has no response

Value of
--PRESP

Value of
--OCCUR

Y
Y
Y

Y
N

Value of
--STAT

NOT DONE

Refer to the standard domains in the Events and Interventions General Observation Classes for additional
assumptions and examples.

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CDISC SDTM Implementation Guide (Version 3.1.2)

5 Models for Special-Purpose Domains
5.1 DEMOGRAPHICS
5.1.1 DEMOGRAPHICS — DM
5B

dm.xpt, Demographics — Version 3.1.2. One record per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char DM
Identifier Two-character abbreviation for the domain.
186H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
359H60

361H

USUBJID

SUBJID
RFSTDTC

Unique Subject Identifier Char

Subject Identifier for the Char
Study
Subject Reference Start Char
Date/Time

RFENDTC Subject Reference End
Date/Time

Char

SITEID

Study Site Identifier

Char

INVID

Investigator Identifier

Char

INVNAM

Investigator Name

Char

BRTHDTC Date/Time of Birth

Char

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November 12, 2008

ISO 8601

Identifier Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product. This must be a unique
number, and could be a compound identifier formed by concatenating
STUDYID-SITEID-SUBJID.
Topic
Subject identifier, which must be unique within the study. Often the ID of
the subject as recorded on a CRF.
Record
Reference Start Date/time for the subject in ISO 8601 character format.
Qualifier Usually equivalent to date/time when subject was first exposed to study
treatment. Required for all randomized subjects; will be null for all
subjects who did not meet the milestone the date requires, such as screen
failures or unassigned subjects.
Record
Reference End Date/time for the subject in ISO 8601 character format.
Qualifier Usually equivalent to the date/time when subject was determined to have
ended the trial, and often equivalent to date/time of last exposure to study
treatment. Required for all randomized subjects; null for screen failures or
unassigned subjects.
Record
Unique identifier for a site within a study.
Qualifier
Record
An identifier to describe the Investigator for the study. May be used in
Qualifier addition to SITEID. Not needed if SITEID is equivalent to INVID.
Synonym Name of the investigator for a site.
Qualifier
Record
Date/time of birth of the subject.
Qualifier

Req
362H

Req
364H

Exp

SDTM 2.2.5,
SDTMIG 4.1.4.1
375H

508H

365H

Exp

SDTM 2.2.5,
SDTMIG 4.1.4.1
375H

Req
Perm
Perm
Perm

SDTM 2.2.5,
SDTMIG 4.1.4.1
36H

375H

CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name

Variable Label

Controlled
Type Terms, Codelist
Role
or Format
Num
Record
Qualifier

AGE

Age

AGEU

Age Units

Char

(AGEU)

SEX

Sex

Char

(SEX)

RACE

Race

Char

(RACE)

1869H

367H

368H

Variable
Qualifier
Record
Qualifier
Record
Qualifier

CDISC Notes

Core

Age expressed in AGEU. May be derived from RFSTDTC and
BRTHDTC, but BRTHDTC may not be available in all cases (due to
subject privacy concerns).
Units associated with AGE.

Exp

Sex of the subject.

Req

References

Exp

Race of the subject. Sponsors should refer to ―Collection of Race and
Ethnicity Data in Clinical Trials‖ (FDA, September 2005) for guidance
regarding the collection of race
(http://www.fda.gov/cder/guidance/5656fnl.htm) See Assumption below
regarding RACE.
Record
The ethnicity of the subject. Sponsors should refer to ―Collection of Race
Qualifier and Ethnicity Data in Clinical Trials‖ (FDA, September 2005) for
guidance regarding the collection of ethnicity
(http://www.fda.gov/cder/guidance/5656fnl.htm).
Record
ARMCD is limited to 20 characters and does not have special character
Qualifier restrictions. The maximum length of ARMCD is longer than for other
―short‖ variables to accommodate the kind of values that are likely to be
needed for crossover trials. For example, if ARMCD values for a sevenperiod crossover were constructed using two-character abbreviations for
each treatment and separating hyphens, the length of ARMCD values
would be 20.
Synonym Name of the Arm to which the subject was assigned.
Qualifier
Record
Country of the investigational site in which the subject participated in the
Qualifier trial.
Timing
Date/time of demographic data collection.

Exp

Timing

Perm

369H

ETHNIC

Ethnicity

Char

(ETHNIC)

ARMCD

Planned Arm Code

Char

(COUNTRY)
ISO 3166
ISO 8601

1870H

Perm

370H

ARM

Description of Planned
Arm
COUNTRY Country
DMDTC

Date/Time of Collection Char

DMDY

Study Day of Collection Num

187H

Req

SDTMIG 4.1.2.1
371H

Req

SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4
372H

37H

Req
Perm

SDTM 2.2.5,
SDTMIG 4.1.4.1
SDTM 2.2.5,
SDTMIG 4.1.4.1
374H

Study day of collection measured as integer days.

375H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)

5.1.1.1 ASSUMPTIONS FOR DEMOGRAPHICS DOMAIN MODEL
1. Investigator and site identification: Companies use different methods to distinguish sites and investigators. CDISC assumes that SITEID will always be
present, with INVID and INVNAM used as necessary. This should be done consistently and the meaning of the variable made clear in the define.xml.
2. Every subject in a study must have a subject identifier (SUBJID). In some cases a subject may participate in more than one study. To identify a subject
uniquely across all studies for all applications or submissions involving the product, a unique identifier (USUBJID) must be included in all datasets.
Subjects occasionally change sites during the course of a clinical trial. The sponsor must decide how to populate variables such as USUBJID, SUBJID
and SITEID based on their operational and analysis needs, but only one DM record should be submitted for the subject. The Supplemental Qualifiers
dataset may be used if appropriate to provide additional information.
3. Concerns for subject privacy suggest caution regarding the collection of variables like BRTHDTC. This variable is included in the Demographics model
in the event that a sponsor intends to submit it; however, sponsors should follow regulatory guidelines and guidance as appropriate.
4. The values of ARM and ARMCD in DM must match entries in the Trial Arms (TA) dataset, except for subjects who were not fully assigned to an Arm.
Subjects who did not receive the treatments to which they were assigned will still have the values of ARM and ARMCD to which they were assigned.
SE/DM Examples 1 and 2 in section 5.3.1.2 show examples of subjects whose actual treatment did not match their planned treatment.
376H

Some subjects may leave the trial before they can be assigned to an Arm, or, in the case of trials where Arm is assigned by two or more successive
allocation processes, may leave before the last of these processes. Such subjects will not be assigned to one of the planned Arms described in the Trial
Arms dataset, and must have special values of ARM and ARMCD assigned.


Data for screen failure subjects, if submitted, should be included in the Demographics dataset, with ARMCD = ―SCRNFAIL" and ARM = ―Screen
Failure‖. Sponsors may include a record in the Disposition dataset indicating when the screen failure event occurred. DM/SE Example 6 shows an
example of data submitted for a screen failure subject.



Some trial designs include Elements after screening but before Arm assignments are made, and so may have subjects who are not screen failures, but are
not assigned to an Arm. Subjects withdrawn from a trial before assignment to an Arm, if they are not screen failures, should have ARMCD =
"NOTASSGN" and ARM = "Not Assigned". Example Trial 1 in Section 7.2.3.1, which includes a screening Epoch and a run-in Epoch before
randomization, is an example of such a trial; data for a subject who passed screening but was not randomized in this trial are shown in DM/SE Example 6.
1872H



37H

In trials where Arm assignment is done by means of two or more allocation processes at separate points in time, subjects who drop out after the first
allocation process but before the last allocation process, should be assigned values of ARMCD that reflect only the allocation processes they
underwent. Example Trial 3, Section 7.2.3.3, is such a trial. DM/SE Example 7 shows sample data for subjects in this trial.
1873H

378H

5. When study population flags are included in SDTM, they are treated as Supplemental Qualifiers (see Section 8.4) to DM and placed in the SUPPDM
dataset. Controlled terms for these subject-level population flags, (e.g., COMPLT, SAFETY, ITT and PPROT) are listed in Appendix C5. See ICH E9 for
more information and definitions. Note that the ADaM subject-level analysis dataset (ADSL) includes population flags; consult the ADaM
Implementation Guide for more information about these variables.
379H

6. Submission of multiple race responses should be represented in the Demographics domain and Supplemental Qualifiers (SUPPDM) dataset as described
in assumption 4.1.2.8.3, Multiple Responses for a Non-Result Qualifier. If multiple races are collected then the value of RACE should be ―MULTIPLE‖
and the additional information will be included in the Supplemental Qualifiers dataset. Controlled terminology for RACE should be used in both DM and
SUPPDM so that consistent values are available for summaries regardless of whether the data are found in a column or row. If multiple races were
collected and one was designated as primary, RACE in DM should be the primary race and additional races should be reported in SUPPDM. When
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additional free text information is reported about subject's RACE using ―Other, Specify‖, Sponsors should refer to Section 4.1.2.7.1. If the race was
381H

collected via an ―Other, Specify‖ field and the sponsor chooses not to map the value as described in the current FDA guidance (see CDISC Notes for RACE) then the
value of RACE should be ―OTHER‖. If a subject refuses to provide race information, the value of RACE could be ―UNKNOWN‖. Examples are provided below in

Section 5.1.1.2.
382H

7. RFSTDTC, RFENDTC, and BRTHDTC represent date/time values, but they are considered to have a Record Qualifier role in DM. They are not
considered to be Timing Variables because they are not intended for use in the general observation classes.
8. Additional Permissible Identifier, Qualifier and Timing Variables
Only the following Timing variables are permissible and may be added as appropriate: VISITNUM, VISIT, VISITDY. The Record Qualifier DMXFN
(External File Name) is the only additional variable that may be added, which is adopted from the Findings general observation class, may also be used to
refer to an external file, such as a patient narrative.
5.1.1.2 EXAMPLES FOR DEMOGRAPHICS DOMAIN MODEL
Examples of using the DM domain for typical scenarios are provided below. Example 1 displays the all Required and Expected variables; in examples 2 - 6,
certain Required or Expected variables have been omitted in consideration of space and clarity. Example 1 is a general Demographics example showing typical
data recorded for a clinical trial. Examples 2 through 5 display various scenarios for representing race and ethnicity information. Example 6 shows the handling
of ARMCD for Subjects Withdrawn before Assignment to an Arm, and Example 7 shows the handling ARMCD for Subjects Withdrawn when assignment to an
Arm is Incomplete.
DM Example 1 – General Demographics
dm.xpt
Row
1
2
3
4
5
6
Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)

STUDYID
ABC123
ABC123
ABC123
ABC123
ABC123
ABC123
SEX
M
M
F
M
F
F

DOMAIN
DM
DM
DM
DM
DM
DM

USUBJID
ABC12301001
ABC12301002
ABC12301003
ABC12301004
ABC12302001
ABC12302002

SUBJID
001
002
003
004
001
002

RFSTDTC
2006-01-12
2006-01-15
2006-01-16

RFENDTC
2006-03-10
2006-02-28
2006-03-19

2006-02-02
2006-02-03

2006-03-31
2006-04-05

RACE
WHITE
WHITE
BLACK OR AFRICAN AMERICAN
ASIAN
AMERICAN INDIAN OR ALASKA NATIVE
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDERS

SITEID
01
01
01
01
02
02

ETHNIC
HISPANIC OR LATINO
NOT HISPANIC OR LATINO
NOT HISPANIC OR LATINO
NOT HISPANIC OR LATINO
NOT HISPANIC OR LATINO
NOT HISPANIC OR LATINO

INVNAM
JOHNSON, M
JOHNSON, M
JOHNSON, M
JOHNSON, M
GONZALEZ, E
GONZALEZ, E
ARMCD
A
P
P
SCRNFAIL
P
A

BIRTHDTC
1948-12-13
1955-03-22
1938-01-19
1941-07-02
1950-06-23
1956-05-05
ARM
Drug A
Placebo
Placebo
Screen Failure
Placebo
Drug A

AGE
57
50
68

AGEU
YEARS
YEARS
YEARS

55
49

YEARS
YEARS
COUNTRY
USA
USA
USA
USA
USA
USA

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CDISC SDTM Implementation Guide (Version 3.1.2)

DM Example 2 – Single Race/Single Ethnicity Choice.
Sample CRF:
Ethnicity

Check one



Hispanic or Latino
Not Hispanic or Latino
Race

Check one

American Indian or Alaska Native
Asian
Black or African American





Native Hawaiian or Other Pacific Islander



White



Row 1: Subject 001 was Not-Hispanic and Asian.
Row 2: Subject 002 was Hispanic and White.
dm.xpt
Row
1
2

STUDYID
ABC
ABC

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November 12, 2008

DOMAIN
DM
DM

USUBJID
001
002

RACE
ASIAN
WHITE

ETHNIC
NOT HISPANIC OR LATINO
HISPANIC OR LATINO

CDISC SDTM Implementation Guide (Version 3.1.2)
DM Example 3 - Multiple Race Choices
In this example, the subject is permitted to check all applicable races.
Sample CRF:
Race

Check all that apply

American Indian or Alaska Native



Asian
Black or African American
Native Hawaiian or Other Pacific Islander





White
Other, Specify: ____________________




Row 1 (DM) and
Row 1 (SUPPDM):
Subject 001 checked ―Other, Specify:‖ and entered ―Brazilian‖ as race.
Row 2 (DM) and
Rows 2, 3, 4, 5 (SUPPDM): Subject 002 checked three races, including an ―Other, Specify‖ value. The three values are reported in SUPPDM using QNAM
values RACE1 - RACE3. The specified information describing other race for is submitted in the same manner as subject 001.
Row 3 (DM):
Subject 003 refused to provide information on race.
Row 4 (DM):
Subject 004 checked ―Asian‖ as their only race.
dm.xpt
Row
1
2
3
4

STUDYID
ABC
ABC
ABC
ABC

DOMAIN
DM
DM
DM
DM

USUBJID
001
002
003
004

RACE
OTHER
MULTIPLE
ASIAN

suppdm.xpt
Row STUDYID
1
ABC
2
ABC
3
ABC
4
ABC
5
ABC

RDOMAIN
DM
DM
DM
DM
DM

USUBJID
001
002
002
002
002

IDVAR

IDVARVAL

QNAM
RACEOTH
RACE1
RACE2
RACE3
RACEOTH

QLABEL
Race, Other
Race 1
Race 2
Race 3
Race, Other

QVAL
BRAZILIAN
BLACK OR AFRICAN AMERICAN
AMERICAN INDIAN OR ALASKA NATIVE
OTHER
ABORIGINE

QORIG
CRF
CRF
CRF
CRF
CRF

QEVAL

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CDISC SDTM Implementation Guide (Version 3.1.2)

DM Example 4: Mapping Predefined Races
In this example, the sponsor has chosen to map some of the predefined races to other races, specifically Japanese and Non-Japanese to Asian. Note: Sponsors
may choose not to map race data, in which case the previous examples should be followed.
Sample CRF
Race
American Indian or Alaska Native

Check One


Asian
Japanese
Non-Japanese
Black or African American
Native Hawaiian or Other Pacific Islander
White







Row 1 (DM), Row 1 (SUPPDM): Subject 001 checked ―Non-Japanese‖ which was mapped by the sponsor to ―Asian‖.
Row 2 (DM), Row 2 (SUPPDM): Subject 002 checked ―Japanese‖ which was mapped by the sponsor to ―Asian‖.
dm.xpt
Row
1
2

STUDYID
ABC
ABC

DOMAIN
DM
DM

USUBJID
001
002

RACE
ASIAN
ASIAN

suppdm.xpt
Row STUDYID
1
ABC
2
ABC

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November 12, 2008

RDOMAIN
DM
DM

USUBJID IDVAR
001
002

IDVARVAL

QNAM
RACEOR
RACEOR

QLABEL
Original Race
Original Race

QVAL
NON-JAPANESE
JAPANESE

QORIG
CRF
CRF

QEVAL

CDISC SDTM Implementation Guide (Version 3.1.2)
DM Example 5: Mapping “Other, Specify” Races.
In this example, the sponsor has chosen to map the values entered into the ―Other, Specify‖ field to one of the preprinted races.
Note: Sponsors may choose not to map race data, in which case the first two examples should be followed.
Sample CRF and Data:
Race
American Indian or Alaska Native

Check One







Asian
Black or African American
Native Hawaiian or Other Pacific Islander
White
Other, Specify: _____________________

Row 1 (DM), Row 1 (SUPPDM): Subject 001 checked ―Other, Specify‖ and entered ―Japanese‖ which was mapped to ―Asian‖ by the sponsor.
Row 2 (DM), Row 2 (SUPPDM): Subject 002 checked ―Other, Specify‖ and entered ―Swedish‖ which was mapped to ―White‖ by the sponsor.
dm.xpt
Row STUDYID
1
ABC
2
ABC

DOMAIN
DM
DM

USUBJID
001
002

RACE
ASIAN
WHITE

suppdm.xpt
Row STUDYID
1
ABC
2
ABC

RDOMAIN
DM
DM

USUBJID
001
002

IDVAR

IDVARVAL

QNAM
RACEOR
RACEOR

QLABEL
Original Race
Original Race

QVAL
JAPANESE
SWEDISH

QORIG QEVAL
CRF
CRF

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DM/SE Example 6
The following examples illustrate values of ARMCD for subjects in Example Trial 1, described in Section 7.2.3.1. The sponsor is submitting data on screenfailure subjects.
Row 1: Subject 001 was randomized to Arm A. Rows 1-3 of SE dataset show that the subject completed all the Elements for Arm A.
Row 2: Subject 002 was randomized to Arm B. Rows 4-6 of SE dataset show that the subject completed all the Elements for Arm B.
Row 3: Subject 003 was a screen failure. Row 7 of SE dataset shows that they passed through only the Screen Element.
Row 4: Subject 004 withdrew during the Run-in Element. They were not considered a screen failure, but they were not randomized, so they have been given the
special ARMCD value NOTASSGN. Rows 8-9 of the SE dataset show the two Elements (Screen and Run-in) this subject passed through.
1874H

38H

dm.xpt
Row STUDYID DOMAIN USUBJID

ARMCD

ABC

001

ABC

002

ABC

003

SCRNFAIL

ABC

004

NOTASSGN

se.xpt
Row
1
2
3
4
5
6
7
8
9

STUDYID
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC

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November 12, 2008

DOMAIN
SE
SE
SE
SE
SE
SE
SE
SE
SE

USUBJID
001
001
001
002
002
002
003
004
004

SESEQ
1
2
3
1
2
3
1
1
2

ETCD
SCRN
RI
A
SCRN
RI
B
SCRN
SCRN
RI

ELEMENT
Screen
Run-In
Drug A
Screen
Run-In
Drug B
Screen
Screen
Run-In

SESTDTC
2006-06-01
2006-06-07
2006-06-21
2006-05-03
2006-05-10
2006-05-24
2006-06-27
2006-05-14
2006-05-21

SEENDTC
2006-06-07
2006-06-21
2006-07-05
2006-05-10
2006-05-24
2006-06-07
2006-06-30
2006-05-21
2006-05-26

CDISC SDTM Implementation Guide (Version 3.1.2)
DM/SE Example 7:
The following example illustrates values of ARMCD for subjects in Example Trial 3, described in Section 7.2.3.3.
Row 1: Subject 001 was randomized to Drug A. At the end of the Double Blind Treatment Epoch, they were assigned to Open Label A. Thus their ARMCD is
AA. Rows 1-3 of the SE dataset show that subject passed through all three Elements for the AA Arm.
Row 2: Subject 002 was randomized to Drug A. They were lost to follow-up during the Double Blind Epoch, so never reached the Open Label Epoch, when they
would have been assigned to either the Open Drug A or the Rescue Element. Their ARMCD is A. Note that A is not one of the Arm code values in the
Trial Arms dataset for this trial. See Section 7.2.4.2 for more information on handling subjects who do not reach all branch points in the trial design.
Rows 4-5 of the SE dataset show the two Elements (Screen and Treatment A) the subject passed through.
1875H

384H

385H

dm.xpt
Row
1
2

STUDYID
DEF
DEF

DOMAIN
DM
DM

USUBJID
001
002

ARMCD
ARM
AA
A-OPEN A
A
A

se.xpt
Row
1
2
3
4
5

STUDYID
DEF
DEF
DEF
DEF
DEF

DOMAIN
SE
SE
SE
SE
SE

USUBJID
001
001
001
002
002

SESEQ
1
2
3
1
2

ETCD
SCRN
DBA
OA
SCRN
DBA

ELEMENT
Screen
Treatment A
Open Drug A
Screen
Treatment A

SESTDTC
2006-01-07
2006-01-12
2006-04-10
2006-02-03
2006-02-10

SEENDTC
2006-01-12
2006-04-10
2006-07-05
2006-02-10
2006-03-24

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CDISC SDTM Implementation Guide (Version 3.1.2)

5.2 COMMENTS
5.2.1 COMMENTS — CO
co.xpt, Comments —Version 3.1.2,One record per comment per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Study Identifier
Char
Identifier Unique identifier for a study.
Domain Abbreviation Char CO
Identifier Two-character abbreviation for the domain.
Variable Label

1876H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
386H7

38H

RDOMAIN Related Domain
Abbreviation
USUBJID Unique Subject
Identifier
COSEQ
Sequence Number

Char

IDVAR

Char

Identifying Variable

Char
Num
*

IDVARVAL Identifying Variable Char
Value
COREF
Comment Reference Char

COVAL

Comment

Char

Record
Two-character abbreviation for the domain of the parent record(s). Null for
Qualifier comments collected on a general comments or additional information CRF page.
Identifier Identifier used to uniquely identify a subject across all studies for all applications
or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a domain.
May be any valid number.
Record
Identifying variable in the parent dataset that identifies the record(s) to which the
Qualifier comment applies. Examples AESEQ or CMGRPID. Used only when individual
comments are related to domain records. Null for comments collected on separate CRFs.
Record
Value of identifying variable of the parent record(s). Used only when individual
Qualifier comments are related to domain records. Null for comments collected on separate CRFs.
Record
Sponsor-defined reference associated with the comment. May be the CRF page
Qualifier number (e.g. 650), or a module name (e.g. DEMOG), or a combination of
information that identifies the reference (e.g. 650-VITALS-VISIT 2).
Topic
The text of the comment. Text over 200 characters can be added to additional
columns COVAL1-COVALn. See assumption 5.2.1.1.3.
Record
Used to describe the originator of the comment. Examples: CENTRAL, REVIEWER,
Qualifier ADJUDICATION COMMITTEE, PRINCIPAL INVESTIGATOR.
Timing
Date/time of comment on dedicated comment form. Should be null if this is a
child record of another domain or if comment date was not collected.

Perm
Req

SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
389H0

Req
Perm

Perm
Perm

Req

391H

COEVAL

Evaluator

Char

CODTC

Date/Time of
Comment

Char

ISO 8601

Perm
Perm

SDTM 2.2.5,
SDTMIG 4.1.4.1
392H

375H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
5.2.1.1 ASSUMPTIONS FOR COMMENTS DOMAIN MODEL
1.

The Comments special-purpose domain provides a solution for submitting free-text comments related to data in one or more SDTM domains (as
described in Section 8.5) or collected on a separate CRF page dedicated to comments. Comments are generally not responses to specific questions;
instead, comments usually consist of voluntary, free-text or unsolicited observations.
39H

The CO dataset accommodates three sources of comments:
a. Those unrelated to a specific domain or parent record(s), in which case the values of the variables RDOMAIN, IDVAR and IDVARVAL are
null. CODTC should be populated if captured. See example, Rows 1.
b. Those related to a domain but not to specific parent record(s), in which case the value of the variable RDOMAIN is set to the DOMAIN code
of the parent domain and the variables IDVAR and IDVARVAL are null. CODTC should be populated if captured. See example, Row 2.
c. Those related to a specific parent record or group of parent records, in which case the value of the variable RDOMAIN is set to the DOMAIN
code of the parent record(s) and the variables IDVAR and IDVARVAL are populated with the key variable name and value of the parent
record(s). Assumptions for populating IDVAR and IDVARVAL are further described in Section 8.5. CODTC should be null because the timing
of the parent record(s) is inherited by the comment record. See example, Rows 3-5.
394H

When the comment text is longer than 200 characters, the first 200 characters of the comment will be in COVAL, the next 200 in COVAL1, and
additional text stored as needed to COVALn. See example, Rows 3-4.

Additional information about how to relate comments to parent SDTM records is provided in Section 8.5.

The variable COREF may be null unless it is used to identify the source of the comment. See example, Rows 1 and 5.

Only following Identifier and Timing variables that are permissible and may be added as appropriate when comments are not related to other
domain records: COGRPID, COREFID, COSPID, VISIT, VISITNUM, VISITDY, TAETORD, CODY, COTPT, COTPTNUM, COELTM,
COTPTREF, CORFTDTC.

395H

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CDISC SDTM Implementation Guide (Version 3.1.2)

5.2.1.2 EXAMPLES FOR COMMENTS DOMAIN MODEL
In the example below:
 Row 1:
Shows a comment unrelated to any specific domain or record, because it was collected on a separate comments page...
 Row 2:
Shows a comment related to a specific domain (PE in this example), but not to any specific record because it was collected on the
bottom of the PE page without any indication of specific records it applies to. COREF is populated with the text ―VISIT 7‖ to show
this comment came from the VISIT 7 PE page.
 Rows 3-5:
Show comments related to parent records in the AE, EX and VS domains.
 Row 3 shows a comment related to a single AE record having it‘s AESEQ=7.
 Row 4 shows a comment related to multiple EX records having their EXGRPID=―COMBO1‖.
 Row 5 shows a comment related to multiple VS records having their VSGRPID=―VS2‖
 Rows 6-8:
Show three options for representing a comment unrelated to any specific general observation class record(s) because it was collected
on a separate comments page, but the page was associated with a specific visit.
 Row 6 shows the comment related to the Subject Visit record in SV. The RDOMAIN variable is populated with SV (the Subject
Visits domain) and the variables IDVAR and IDVARVAL are populated with the key variable name and value of the parent
Subject-Visit record.
 Row 7 shows the comment unrelated to any parent records, RDOMAIN, IDVAR and IDVARVAL are not populated. COREF is
populated to indicate that the comment reference is ―VISIT 4‖
 Row 8 also shows the comment unrelated to any parent records, but instead of populating COREF, the VISIT Timing variable was
added to the CO dataset and populated with 4 to indicate Visit 4.
Row STUDYID

DOMAIN

USUBJID

COSEQ

RDOMAIN

IDVAR

IDVARVAL

COREF

COVAL

COVAL1

COVAL2

COEVAL

2003-11-08

Comment text

PRINCIPAL
INVESTIGATOR

2004-01-14

1234

AB-99

1234

AB-99

1234

AB-99

AESEQ

PAGE 650

First 200
characters

Next 200
characters

1234

AB-99

EXGRPID

COMBO1

PAGE 320-355

First 200
characters

Remaining text

1234

AB-99

VSGRPID

VS2

Comment text

PRINCIPAL
INVESTIGATOR

1234

AB-99

VISITNUM

Comment Text

PRINCIPAL
INVESTIGATOR

1234

AB-99

Comment Text

PRINCIPAL
INVESTIGATOR

1234

AB-99

Comment Text

PRINCIPAL
INVESTIGATOR

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November 12, 2008

VISIT 4

CODTC

Comment text

VISIT 7

VISIT

PRINCIPAL
INVESTIGATOR

Remaining text

PRINCIPAL
INVESTIGATOR
PRINCIPAL
INVESTIGATOR

CDISC SDTM Implementation Guide (Version 3.1.2)

5.3 SUBJECT ELEMENTS AND VISITS
The Trial Elements, Trial Arms, and Trial Visits datasets in the Trial Design model describe the planned design of the study (see Section 7.3, Section 7.2 and
Section 7.4), but it is also necessary to collect the corresponding actual data. Subject assignment to an Arm is reported in the ARM variable in Demographics.
Actual Elements and Visits data for each subject are described in two additional datasets:
 The Subject Elements dataset (Table 5.3.1)
 The Subject Visits dataset (Table 5.3.2).
396H

397H

398H

5.3.1 SUBJECT ELEMENTS — SE
The Subject Elements dataset consolidates information about the timing of each subject‘s progress through the Epochs and Elements of the trial. For Elements
that involve study treatments, the identification of which Element the subject passed through (e.g., Drug X vs. placebo) is likely to derive from data in the
Exposure domain or another Interventions domain. The dates of a subject‘s transition from one Element to the next will be taken from the Interventions
domain(s) and from other relevant domains, according to the definitions (TESTRL values) in the Trial Elements dataset (see Section 7.3).
39H

The Subject Elements dataset is particularly useful for studies with multiple treatment periods, such as crossover studies. The Subject Elements dataset contains
the date/times at which a subject moved from one Element to another, so when the Trial Arms ( Section 7.2), Trial Elements (Section 7.3), and Subject Elements
datasets are included in a submission, reviewers can relate all the observations made about a subject to that subject‘s progression through the trial.
 Comparison of the --DTC of a finding observation to the Element transition dates (values of SESTDTC and SEENDTC) tells which Element the subject
was in at the time of the finding. Similarly, one can determine the Element during which an event or intervention started or ended.
 ―Day within Element‖ or ―day within Epoch‖ can be derived. Such variables relate an observation to the start of an Element or Epoch in the same way
that study day (--DY) variables relate it to the reference start date (RFSTDTC) for the study as a whole. See Section 4.1.4.4
 Having knowledge of Subject Element start and end dates can be helpful in the determination of baseline values.
40H

401H

402H

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CDISC SDTM Implementation Guide (Version 3.1.2)

se.xpt, Subject Elements — Version 3.1.2. One record per actual Element per subject.
Variable
Name

Variable Label

STUDYID Study Identifier
DOMAIN Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char SE
Identifier Two-character abbreviation for the domain.
187H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
403H

405H

USUBJID

Unique Subject Identifier Char

SESEQ

Sequence Number

Num

ETCD

Element Code

Char

ELEMENT Description of Element

Char

SESTDTC

Char
Char

Start Date/Time of
Element
SEENDTC End Date/Time of
Element
TAETORD Planned Order of
Elements within Arm
EPOCH
Epoch

Req

ISO 8601

Identifier Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. Should be assigned to be consistent chronological order.
Topic
1. ETCD (the companion to ELEMENT) is limited to 8 characters and does
not have special character restrictions. These values should be short for ease
of use in programming, but it is not expected that ETCD will need to serve as
a variable name.
2. If an encountered Element differs from the planned Element to the point
that it is considered a new Element, then use ―UNPLAN‖ as the value for
ETCD to represent this Element.
Synonym The name of the Element. If ETCD has a value of ―UNPLAN‖ then
Qualifier ELEMENT should be Null.
Timing
Start date/time for an Element for each subject.

ISO 8601

Timing

Exp

406H7

Req
Req

SDTMIG 4.1.2.1

Perm

408H

SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4
SDTM 2.2.5,
SDTMIG 4.1.4.1
SDTM 2.2.5,
SDTMIG 4.1.4.1
409H

410H

Req
41H

End date/time for an Element for each subject.

412H

Num
Char

SEUPDES Description of Unplanned Char
Element

Timing
*

Number that gives the planned order of the Element within the subject's
Perm
assigned ARM.
Timing
Epoch associated with the Element in the planned sequence of Elements for Perm
the ARM to which the subject was assigned
Synonym Description of what happened to the subject during this unplanned Element. Perm
Qualifier Used only if ETCD has the value of ―UNPLAN‖.

SDTM 2.2.5,
SDTMIG 7.1.2
413H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
5.3.1.1 ASSUMPTIONS FOR SUBJECT ELEMENTS DOMAIN MODEL
1.

Submission of the Subject Elements dataset is strongly recommended, as it provides information needed by reviewers to place observations in context within
the study. The Trial Elements and Trial Arms datasets should also be submitted, as they define the design and the terms referenced by the Subject Elements
dataset.

The Subject Elements domain allows the submission of data on the timing of the trial Elements a subject actually passed through in their participation in the
trial. Please read Section 7.3, on the Trial Elements dataset and Section 7.2, on the Trial Arms dataset, as these datasets define a trial's planned Elements, and
describe the planned sequences of Elements for the Arms of the trial.
41H

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CDISC SDTM Implementation Guide (Version 3.1.2)
3.

For any particular subject, the dates in the subject Elements table are the dates when the transition events identified in the Trial Elements table occurred. Judgment may be
needed to match actual events in a subject's experience with the definitions of transition events (the events that mark the starts of new Elements) in the Trial Elements table,
since actual events may vary from the plan. For instance, in a single dose PK study, the transition events might correspond to study drug doses of 5 and 10 mg. If a subject
actually received a dose of 7 mg when they were scheduled to receive 5 mg, a decision will have to be made on how to represent this in the SE domain.

If the date/time of a transition Element was not collected directly, the method used to infer the Element start date/time should be explained in the Comments
column of the define.xml.

Judgment will also have to be used in deciding how to represent a subject's experience if an Element does not proceed or end as planned. For instance, the
plan might identify a trial Element which is to start with the first of a series of 5 daily doses and end after 1 week, when the subject transitions to the next
treatment Element. If the subject actually started the next treatment Epoch (see Section 7.1.2) after 4 weeks, the sponsor will have to decide whether to
represent this as an abnormally long Element, or as a normal Element plus an unplanned non-treatment Element.
416H

If the sponsor decides that the subject's experience for a particular period of time cannot be represented with one of the planned Elements, then that period of
time should be represented as an unplanned Element. The value of ETCD for an unplanned Element is ―UNPLAN‖ and SEUPDES should be populated with
a description of the unplanned Element.

The values of SESTDTC provide the chronological order of the actual subject Elements. SESEQ should be assigned to be consistent with the chronological order. Note that
the requirement that SESEQ be consistent with chronological order is more stringent than in most other domains, where --SEQ values need only be unique within subject.

When TAETORD is included in the SE domain, it represents the planned order of an Element in an Arm. This should not be confused with the actual order of the
Elements, which will be represented by their chronological order and SESEQ. TAETORD will not be populated for subject Elements that are not planned for the
Arm to which the subject was assigned. Thus, TAETORD will not be populated for any Element with an ETCD value of ―UNPLAN‖. TAETORD will also not be
populated if a subject passed through an Element that, although defined in the TE dataset, was out of place for the Arm to which the subject was assigned. For
example, if a subject in a parallel study of Drug A vs. Drug B was assigned to receive Drug A, but received Drug B instead, then TAETORD would be left blank
for the SE record for their Drug B Element. If a subject was assigned to receive the sequence of Elements A, B, C, D, and instead received A, D, B, C, then the
sponsor would have to decide for which of these subject Element records TAETORD should be populated. The rationale for this decision should be documented
in the Comments column of the define.xml.

For subjects who follow the planned sequence of Elements for the Arm to which they were assigned, the values of EPOCH in the SE domain will match
those associated with the Elements for the subject's Arm in the Trial Arms dataset. The sponsor will have to decide what value, if any, of EPOCH to assign
SE records for unplanned Elements and in other cases where the subject's actual Elements deviate from the plan. The sponsor's methods for such decisions
should be documented in the define.xml, in the row for EPOCH in the SE dataset table.

10. Since there are, by definition, no gaps between Elements, the value of SEENDTC for one Element will always be the same as the value of SESTDTC for the next Element.
11. Note that SESTDTC is required, although --STDTC is not required in any other subject-level dataset. The purpose of the dataset is to record the Elements a
subject actually passed through. We assume that if it is known that a subject passed through a particular Element, then there must be some information on when
it started, even if that information is imprecise. Thus, SESTDTC may not be null, although some records may not have all the components (e.g., year, month,
day, hour, minute) of the date/time value collected.
12. The following Identifier variables are permissible and may be added as appropriate: --GRPID, --REFID, --SPID.
13. Care should be taken in adding additional Timing variables:

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CDISC SDTM Implementation Guide (Version 3.1.2)



The purpose of --DTC and --DY in other domains with start and end dates (Event and Intervention Domains) is to record the date and study day on
which data was collected. The starts and ends of elements are generally ―derived‖ in the sense that they are a secondary use of data collected
elsewhere, and it is not generally useful to know when those date/times were recorded.



--DUR could be added only if the duration of an element was collected, not derived.



It would be inappropriate to add the variables that support time points (--TPT, --TPTNUM, --ELTM, --TPTREF, and --RFTDTC), since the topic of
this dataset is Elements.

5.3.1.2 EXAMPLES FOR SUBJECT ELEMENTS DOMAIN MODEL
STUDYID and DOMAIN, which are required in the SE and DM domains, have not been included in the following examples, to improve readability.
Example 1
This example shows data for two subjects for a crossover trial with four Epochs.
Row 1: The record for the SCREEN Element for subject 789. Note that only the date of the start of the SCREEN Element was collected, while for the end
of the Element, which corresponds to the start of IV dosing, both date and time were collected.
Row 2: The record for the IV Element for subject 789. The IV Element started with the start of IV dosing and ended with the start of oral dosing, and full
date/times were collected for both.
Row 3: The record for the ORAL Element for subject 789. Only the date, and not the time, of start of Follow-up was collected.
Row 4: The FOLLOWUP Element for subject 789 started and ended on the same day. Presumably, the Element had a positive duration, but no times were
collected.
Rows 5-8: Subject 790 was treated incorrectly, as shown by the fact that the values of SESEQ and TAETORD do not match. This subject entered the IV
Element before the Oral Element, but the planned order of Elements for this subject was ORAL, then IV. The sponsor has assigned EPOCH
values for this subject according to the actual order of Elements, rather than the planned order. The correct order of Elements is the subject's
ARMCD, shown in Row 2 of the DM dataset.
se.xpt
Row
1
2
3
4
5
6
7
8

USUBJID
789
789
789
789
790
790
790
790

Page 70
November 12, 2008

SESEQ
1
2
3
4
1
2
3
4

ETCD
SCREEN
IV
ORAL
FOLLOWUP
SCREEN
IV
ORAL
FOLLOWUP

SESTDTC
2006-06-01
2006-06-03T10:32
2006-06-10T09:47
2006-06-17
2006-06-01
2006-06-03T10:14
2006-06-10T10:32
2006-06-17

SEENDTC
2006-06-03T10:32
2006-06-10T09:47
2006-06-17
2006-06-17
2006-06-03T10:14
2006-06-10T10:32
2006-06-17
2006-06-17

SEUPDES

TAETORD
1
2
3
4
1
3
2
4

EPOCH
SCREEN
FIRST TREATMENT
SECOND TREATMENT
FOLLOW-UP
SCREEN
FIRST TREATMENT
SECOND TREATMENT
FOLLOW-UP

CDISC SDTM Implementation Guide (Version 3.1.2)
dm.xpt
Row

USUBJID

SUBJID

RFSTDTC

RFENDTC

SITEID

INVNAM

BIRTHDTC

AGE

AGEU

SEX

RACE

789

001

2006-06-03

2006-06-17

SMITH, J

1948-12-13

YEARS

WHITE

790

002

2006-06-03

2006-06-17

SMITH, J

1955-03-22

YEARS

WHITE

ETHNIC
HISPANIC OR
LATINO
NOT
HISPANIC OR
LATINO

ARMCD

ARM

COUNTRY

IV-ORAL

USA

ORAL-IV

USA

Example 2
The data below represent two subjects enrolled in Example Trial 3, described in Section 7.2.3.3.
Rows 1-2: Subject 123 completed only two Elements of the trial. The double-blind treatment Epoch starts with the start of dosing, but in this trial only the
date, and not the time, of the start of dosing has been collected. Note that, for this subject, events that occurred on, or data collected on, 2006-0603 cannot be assigned to an Element or an Epoch on the basis of dates alone. When sponsors choose to collect only dates, they must deal with
such ambiguity in the algorithms they use to assign data to Elements or Epochs. Row 1 of the Demographics dataset shows that this subject has an
ARMCD value of A. See DM/SE Example 6 in Section 5.1.1.2 for other examples of ARM and ARMCD values for this trial.
Rows 3-6: Subject 456 completed the trial, but received the wrong drug for the last 2 weeks of the double-blind treatment period. This has been represented
by treating the period when the subject received the wrong drug as an unplanned Element. Note that TAETORD, which represents the planned
order of Elements within an Arm, has not been populated for this unplanned Element. However, even though this Element was unplanned, the
sponsor assigned a value of DOUBLE BLIND TREATMENT to EPOCH. Row 2 of the Demographics dataset shows that the values of ARM and
ARMCD for this subject reflect their planned treatment, and are not affected by the fact that their treatment deviated from plan.
187H

417H

418H

se.xpt
Row
1
2
3
4
5
6

USUBJID
123
123
456
456
456
456

SESEQ
1
2
1
2
3
4

ETCD
SCRN
DBA
SCRN
DBA
UNPLAN
RSC

SESTDTC
2006-06-01
2006-06-03
2006-05-01
2006-05-03
2006-05-31
2006-06-13

SEENDTC
2006-06-03
2006-06-10
2006-05-03
2006-05-31
2006-06-13
2006-07-30

SEUPDES

TAETORD
1
2
1
2

Drug B dispensed in error
3

EPOCH
SCREEN
DOUBLE-BLIND TREATMENT
SCREEN
DOUBLE-BLIND TREATMENT
DOUBLE-BLIND TREATMENT
OPEN-LABEL TREATMENT

dm.xpt
Row

USUBJID

SUBJID

RFSTDTC

RFENDTC

SITEID

INVNAM

BIRTHDTC

AGE

AGEU

SEX

RACE

123

012

2006-06-03

2006-06-10

JONES, D

1943-12-08

YEARS

ASIAN

456

103

2006-05-03

2006-07-30

JONES, D

1950-05-15

YEARS

WHITE

ETHNIC
HISPANIC
OR
LATINO
NOT
HISPANIC
OR
LATINO

ARMCD

ARM

COUNTRY

USA

ARescue

USA

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CDISC SDTM Implementation Guide (Version 3.1.2)

5.3.2 SUBJECT VISITS — SV
The Subject Visits domain consolidates information about the timing of subject visits that is otherwise spread over domains that include the visit variables (VISITNUM and
possibly VISIT and/or VISITDY). Unless the beginning and end of each visit is collected, populating the Subject Visits dataset will involve derivations. In a simple case,
where, for each subject visit, exactly one date appears in every such domain, the Subject Visits dataset can be created easily, by populating both SVSTDTC and SVENDTC
with the single date for a visit. When there are multiple dates and/or date/times for a visit for a particular subject, the derivation of values for SVSTDTC and SVENDTC may
be more complex. The method for deriving these values should be consistent with the visit definitions in the Trial Visits dataset (see Section 7.4). For some studies, a visit
may be defined to correspond with a clinic visit that occurs within one day, while for other studies, a visit may reflect data collection over a multi-day period.
419H

The Subject Visits dataset provides reviewers with a summary of a subject‘s Visits. Comparison of an individual subject‘s SV dataset with the TV dataset (Section 7.4),
which describes the planned Visits for the trial, quickly identifies missed Visits and ―extra‖ Visits. Comparison of the values of STVSDY and SVENDY to VISIT and/or
VISITDY can often highlight departures from the planned timing of Visits.
420H

sv.xpt, Subject Visits — Version 3.1.2,. One record per subject per actual visit.
Variable
Name
STUDYID
DOMAIN

Variable Label

Controlled Terms,
Codelist or Format

Type

Study Identifier
Char
Domain Abbreviation Char SV
1879H

Role

CDISC Notes

Identifier Unique identifier for a study.
Identifier Two-character abbreviation for the domain.

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1
SDTM 2.2.5,
SDTMIG 4.1.4.1
SDTM 2.2.5,
SDTMIG 4.1.4.4
SDTM 2.2.5,
SDTMIG 4.1.4.4
421H

423H

USUBJID
VISITNUM

VISIT

VISITDY

Unique Subject
Identifier
Visit Number

Visit Name

Char

Identifier Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Topic
1. Clinical encounter number. (Decimal numbering may be useful for
inserting unplanned visits.)
2. Numeric version of VISIT, used for sorting.
Synonym 1. Protocol-defined description of clinical encounter.
Qualifier 2. May be used in addition to VISITNUM and/or VISITDY as a text
description of the clinical encounter.
Timing
Planned study day of the start of the visit based upon RFSTDTC in
Demographics.

Req

Char ISO 8601

Timing

Start date/time for a Visit.

Exp

Char ISO 8601

Timing

End date/time of a Visit.

Exp

Num

Timing

Study day of start of visit relative to the sponsor-defined RFSTDTC.

Perm

Num

Timing

Study day of end of visit relative to the sponsor-defined RFSTDTC.

Perm

Char

Synonym Description of what happened to the subject during an unplanned visit.
Qualifier

Num

Char

Planned Study Day of Num
Visit

42H5

Req
426H

427H

Perm
428H

429H

Perm
430H

431H

SVSTDTC
SVENDTC
SVSTDY
SVENDY
SVUPDES

Start Date/Time of
Visit
End Date/Time of
Visit
Study Day of Start of
Visit
Study Day of End of
Visit
Description of
Unplanned Visit

432H

43H

435H

Perm

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
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CDISC SDTM Implementation Guide (Version 3.1.2)

5.3.2.1 ASSUMPTIONS FOR SUBJECT VISITS DOMAIN MODEL
1.

The Subject Visits domain allows the submission of data on the timing of the trial visits a subject actually passed through in their participation in the
trial. Please read Section 7.4 on the Trial Visits dataset, as the Trial Visits dataset defines the planned visits for the trial.
436H

The identification of an actual visit with a planned visit sometimes calls for judgment. In general, data collection forms are prepared for particular visits,
and the fact that data was collected on a form labeled with a planned visit is sufficient to make the association. Occasionally, the association will not be
so clear, and the sponsor will need to make decisions about how to label actual visits. The sponsor's rules for making such decisions should be
documented in the define.xml document.

Records for unplanned visits should be included in the SV dataset. For unplanned visits, SVUPDES should be populated with a description of the reason
for the unplanned visit. Some judgment may be required to determine what constitutes an unplanned visit. When data are collected outside a planned
visit, that act of collecting data may or may not be described as a "visit." The encounter should generally be treated as a visit if data from the encounter
are included in any domain for which VISITNUM is included, since a record with a missing value for VISITNUM is generally less useful than a record
with VISITNUM populated. If the occasion is considered a visit, its date/times must be included in the SV table and a value of VISITNUM must be
assigned. See Section 4.1.4.5 for information on the population of visit variables for unplanned visits.
437H

VISITDY is the Planned Study Day of a visit. It should not be populated for unplanned visits.

If SVSTDY is included, it is the actual study day corresponding to SVSTDTC. In studies for which VISITDY has been populated, it may be desirable to
populate SVSTDY, as this will facilitate the comparison of planned (VISITDY) and actual (SVSTDY) study days for the start of a visit.

If SVENDY is included, it is the actual day corresponding to SVENDTC.

For many studies, all visits are assumed to occur within one calendar day, and only one date is collected for the Visit. In such a case, the values for
SVENDTC duplicate values in SVSTDTC. However, if the data for a visit is actually collected over several physical visits and/or over several days,
then SVSTDTC and SVENDTC should reflect this fact. Note that it is fairly common for screening data to be collected over several days, but for the
data to be treated as belonging to a single planned screening visit, even in studies for which all other visits are single-day visits.

Differentiating between planned and unplanned visits may be challenging if unplanned assessments (e.g., repeat labs) are performed during the time
period of a planned visit.

Algorithms for populating SVSTDTC and SVENDTC from the dates of assessments performed at a visit may be particularly challenging for screening
visits since baseline values collected at a screening visit are sometimes historical data from tests performed before the subject started screening for the
trial

10. The following Identifier variables are permissible and may be added as appropriate: --SEQ, --GRPID, --REFID, and --SPID.
11. Care should be taken in adding additional Timing variables:
 If TAETORD and/or EPOCH are added, then the values must be those at the start of the visit.
 The purpose of --DTC and --DY in other domains with start and end dates (Event and Intervention Domains) is to record the data on which data
was collected. It seems unnecessary to record the date on which the start and end of a visit were recorded.
 --DUR could be added if the duration of a visit was collected.

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CDISC SDTM Implementation Guide (Version 3.1.2)





It would be inappropriate to add the variables that support time points (--TPT, --TPTNUM, --ELTM, --TPTREF, and --RFTDTC), since the topic
of this dataset is visits.
--STRF and --ENRF could be used to say whether a visit started and ended before, during, or after the study reference period, although this seems
unnecessary.
--STRTPT, --STTPT, --ENRTPT, and --ENTPT could be used to say that a visit started or ended before or after particular dates, although this
seems unnecessary.

5.3.2.2 EXAMPLES FOR SUBJECT VISITS DOMAIN MODEL
The data below represents the visits for a single subject.
Row 1: Data for the screening visit was actually gathered over the course of six days.
Row 2: The visit called DAY 1 actually started and ended as planned, on Day 1.
Row 3: The visit scheduled for Day 8 occurred one day early, on Day 7.
Row 4: The visit called WEEK 2 actually started and ended as planned, on Day 15,
Row 5: Shows an unscheduled visit. SVUPDES provides the information that this visit dealt with evaluation of an adverse event. Since this visit was not
planned, VISITDY was not populated. The sponsor chose not to populate VISIT. VISITNUM was populated, probably because the data collected at
this encounter is in a Findings domain such as EG, LB, or VS, in which VISIT is treated as an important timing variable.
Row 6: This subject had their last visit, a follow-up visit on study Day 26, eight days after the unscheduled visit, but well before the scheduled visit day of
71.
Row
1
2
3
4
5
6

STUDYID
123456
123456
123456
123456
123456
123456

Page 74
November 12, 2008

DOMAIN
SV
SV
SV
SV
SV
SV

USUBJID
101
101
101
101
101
101

VISITNUM
1
2
3
4
4.1
8

VISIT
SCREEN
DAY 1
WEEK 1
WEEK 2

VISITDY
-7
1
8
15

FOLLOW-UP

SVSTDTC
2006-01-15
2006-01-21
2006-01-27
2006-02-04
2006-02-07
2006-02-15

SVENDTC
2006-01-20
2006-01-21
2006-01-27
2006-02-04
2006-02-07
2006-02-15

SVSTDY
-6
1
7
15
18
26

SVENDY
-1
1
7
15
18
26

SVUPDES

Evaluation of AE

CDISC SDTM Implementation Guide (Version 3.1.2)

6 Domain Models Based on the General
Observation Classes
6.1 INTERVENTIONS
6.1.1 CONCOMITANT MEDICATIONS — CM
cm.xpt, Concomitant Medications — Interventions, Version 3.1.2,. One record per recorded intervention occurrence or constant-dosing interval per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char CM
Identifier Two-character abbreviation for the domain.
180H

Core

References

Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
438H9

40H

USUBJID

Unique Subject Identifier

Char

CMSEQ

Sequence Number

Num

CMGRPID

Group ID

Char

CMSPID

Sponsor-Defined Identifier Char

CMTRT

Reported Name of Drug,
Med, or Therapy
CMMODIFY Modified Reported Name

Char

CMDECOD Standardized Medication
Name

Char *

Perm

SDTM 2.2.4
SDTMIG 4.1.2.6
SDTM 2.2.4

Req

SDTM 2.2.1

Perm

SDTM 2.2.1,
SDTMIG 4.1.3.6
SDTM 2.2.1,
SDTMIG 4.1.3.6

43H

Char

Identifier Sponsor-defined reference number. Examples: a number pre-printed on the CRF
as an explicit line identifier or record identifier defined in the sponsor‘s
operational database. Example: line number on a concomitant medication page.
Topic
Verbatim medication name that is either pre-printed or collected on a
CRF.
Synonym If CMTRT is modified to facilitate coding, then CMMODIFY will
Qualifier contain the modified text.
Synonym Standardized or dictionary-derived text description of CMTRT or
Qualifier CMMODIFY. Equivalent to the generic medication name in WHO Drug.

4H5

Perm
46H

The sponsor is expected to provide the dictionary name and
version used to map the terms utilizing the define.xml external
codelist attributes. If an intervention term does not have a decode value in
the dictionary then CMDECOD will be left blank.
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Final

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
CMCAT

CMSCAT
CMPRESP

Variable Label
Category for Medication

Subcategory for
Medication
CM Pre-Specified

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char *
Grouping Used to define a category of medications/treatments. Examples: PRIOR,
Qualifier CONCOMITANT, ANTI-CANCER MEDICATION, or GENERAL
CONMED.
Char *
Grouping A further categorization of medications/ treatment. Examples:
Qualifier CHEMOTHERAPY, HORMONAL THERAPY, ALTERNATIVE THERAPY.
Char (NY)
Record
Used to indicate whether (Y/null) information about the use of a specific
Qualifier medication was solicited on the CRF.
18H

Core

References

Perm

SDTM 2.2.1,
SDTMIG 4.1.2.6
47H

Perm

SDTM 2.2.1,
SDTMIG 4.1.2.6
SDTM 2.2.1,
SDTMIG 4.1.2.7,
SDTMIG 4.1.5.7
SDTM 2.2.1,
SDTMIG 4.1.5.7
48H

Perm
49H50

451H

CMOCCUR CM Occurrence

CMSTAT

Completion Status

Char (NY)
182H

Char (ND)
183H

Record
Qualifier
Record
Qualifier

When the use of specific medications is solicited, CMOCCUR is used to Perm
indicate whether or not (Y/N) use of the medication occurred. Values are
null for medications not specifically solicited.
Used to indicate that a question about a pre-specified medication was not Perm
answered. Should be null or have a value of NOT DONE.

Record
Qualifier

Describes the reason concomitant medication was not collected. Used in
conjunction with CMSTAT when value is NOT DONE.

Record
Qualifier
Variable
Qualifier

Denotes why a medication was taken or administered. Examples:
Perm
NAUSEA, HYPERTENSION.
Drug class. May be obtained from coding. When coding to a single class, Perm
populate with class value. If using a dictionary and coding to multiple
classes, then follow assumption 4.1.2.8.3 or omit CMCLAS.
Class code corresponding to CMCLAS. Drug class. May be obtained from Perm
coding. When coding to a single class, populate with class code. If using a
dictionary and coding to multiple classes, then follow assumption
4.1.2.8.3 or omit CMCLASCD.
Amount of CMTRT taken.
Perm

452H

SDTM 2.2.1,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.1,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.1,
SDTMIG 4.1.5.6
SDTM 2.2.1,
SDTMIG 4.1.3.5
453H

45H

CMREASND Reason Medication Not
Collected

Char

CMINDC

Indication

Char

CMCLAS

Medication Class

Char *

Perm
456H

457H

458H

460H123

459H

CMCLASCD Medication Class Code

Char *

Variable
Qualifier

SDTM 2.2.1,
SDTMIG 4.1.3.5
465H78

46H

CMDOSE

Dose per Administration

Num

CMDOSTXT Dose Description

Char

CMDOSU

Dose Units

Char (UNIT)

CMDOSFRM Dose Form

Char (FRM)

CMDOSFRQ Dosing Frequency per
Interval

Char (FREQ)

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November 12, 2008

469H

184H

472H

Record
Qualifier
Record
Qualifier
Variable
Qualifier
Record
Qualifier
Variable
Qualifier

SDTM 2.2.1

Dosing amounts or a range of dosing information collected in text form. Perm
Units may be stored in CMDOSU. Example: 200-400, 15-20.
Units for CMDOSE, CMDOSTXT, and CMDOSTOT. Examples: ng, mg, Perm
or mg/kg.
Dose form for CMTRT. Examples: TABLET, LOTION.
Perm

SDTM 2.2.1

Usually expressed as the number of repeated administrations of CMDOSE
within a specific time period. Examples: BID (twice daily), Q12H (every 12
hours).

SDTM 2.2.1

Perm

SDTM 2.2.1,
SDTMIG 4.1.3.2
SDTM 2.2.1
470H1

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name

Variable Label

CMDOSTOT Total Daily Dose

Controlled
Type Terms, Codelist
Role
or Format
Num
Record
Qualifier

CMDOSRGM Intended Dose Regimen

Char

CMROUTE

Route of Administration

Char (ROUTE)

CMSTDTC

Start Date/Time of
Medication

Char ISO 8601

End Date/Time of
Medication

Char ISO 8601

Study Day of Start of
Medication

Num

Study Day of End of
Medication

Num

CMDUR

Duration of Medication

Char ISO 8601

CMSTRF

Start Relative to Reference Char (STENRF)
Period

Timing

End Relative to Reference Char (STENRF)
Period

Timing

185H

Variable
Qualifier
Variable
Qualifier
Timing

CDISC Notes

Core

References

Total daily dose of CMTRT using the units in CMDOSU. Total dose over Perm
a period other than day could be recorded in a separate Supplemental
Qualifier variable. CMDOSTOT should be used in addition to CMDOSE,
and not in place of it.
Text description of the (intended) schedule or regimen for the
Perm
Intervention. Examples: TWO WEEKS ON, TWO WEEKS OFF.
Route of administration for CMTRT. Examples: ORAL,
Perm
INTRAVENOUS.
Perm

SDTM 2.2.1

SDTM 2.2.1
SDTM 2.2.1
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.7
473H

47H5

CMENDTC

Timing

Perm
476H

47H

CMSTDY

Timing

Study day of start of medication relative to the sponsor-defined
RFSTDTC.

Perm

Study day of end of medication relative to the sponsor-defined
RFSTDTC.

Perm

Collected duration for a treatment episode. Used only if collected on the
CRF and not derived from start and end date/times.
Describes the start of the medication relative to sponsor-defined reference
period. The sponsor-defined reference period is a continuous period of
time defined by a discrete starting point and a discrete ending point
(represented by RFSTDTC and RFENDTC in Demographics). If
information such as "PRIOR", "ONGOING", or "CONTINUING" was
collected, this information may be translated into CMSTRF.
Describes the end of the medication relative to the sponsor-defined
reference period. The sponsor-defined reference period is a continuous
period of time defined by a discrete starting point and a discrete ending
point (represented by RFSTDTC and RFENDTC in Demographics). If
information such as "PRIOR", "ONGOING", or "CONTINUING" was
collected, this information may be translated into CMENRF.
Identifies the start of the medication as being before or after the reference
time point defined by variable CMSTTPT.

Perm

478H

479H

CMENDY

Timing

480H

481H

CMENRF

186H

187H

Timing

CMSTRTPT Start Relative to Reference Char BEFORE,
Timing
Time Point
COINCIDENT,
AFTER, U
CMSTTPT Start Reference Time Point Char
Timing

482H

Perm
483H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.7
485H6

Perm

Description or date/time in ISO 8601 character format of the reference
Perm
point referred to by CMSTRTPT. Examples: "2003-12-15" or "VISIT 1".

SDTM 2.2.5,
SDTMIG 4.1.4.7
487H

SDTM 2.2.5,
SDTMIG 4.1.4.7
489H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Controlled
Type Terms, Codelist
Role
or Format
CMENRTPT End Relative to Reference Char BEFORE,
Timing
Time Point
COINCIDENT,
AFTER,
ONGOING, U
CMENTPT End Reference Time Point Char
Timing
Variable
Name

Variable Label

CDISC Notes

Core

References

Identifies the end of the medication as being before or after the reference Perm
time point defined by variable CMENTPT.

SDTM 2.2.5,
SDTMIG 4.1.4.7

Description or date/time in ISO 8601 character format of the reference
Perm
point referred to by CMENRTPT. Examples: "2003-12-25" or "VISIT 2".

SDTM 2.2.5,
SDTMIG 4.1.4.7

490H

491H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.1.1.1 ASSUMPTIONS FOR CONCOMITANT MEDICATIONS DOMAIN MODEL
1.

CM Definition and Structure
a. CRF data that captures the Concomitant and Prior Medications/Therapies used by the subject. Examples are the Concomitant Medications/Therapies
given on an as-needed basis and the usual and background medications/therapies given for a condition.
b. The structure of the CM domain is one record per medication intervention episode, constant-dosing interval, or pre-specified medication assessment per
subject. It is the sponsor's responsibility to define an intervention episode. This definition may vary based on the sponsor's requirements for review and
analysis. The submission dataset structure may differ from the structure used for collection. One common approach is to submit a new record when there
is a change in the dosing regimen. Another approach is to collapse all records for a medication to a summary level with either a dose range or the highest
dose level. Other approaches may also be reasonable as long as they meet the sponsor's evaluation requirements.

Concomitant Medications Description and Coding
a. CMTRT captures the name of the Concomitant Medications/Therapy and it is the topic variable. It is a required variable and must have a value. CMTRT
should only include the medication/therapy name and should not include dosage, formulation, or other qualifying information. For example, ―ASPIRIN
100MG TABLET‖ is not a valid value for CMTRT. This example should be expressed as CMTRT= ―ASPIRIN‖, CMDOSE= ―100‖, CMDOSU= ―MG‖,
and CMDOSFRM= ―TABLET‖.
b. CMMODIFY should be included if the sponsor‘s procedure permits modification of a verbatim term for coding.
c. CMDECOD is the standardized medication/therapy term derived by the sponsor from the coding dictionary. It is expected that the reported term
(CMTRT) or the modified term (CMMODIFY) will be coded using a standard dictionary. The sponsor is expected to provide the dictionary name and
version used to map the terms utilizing the define.xml external codelist attributes.

Pre-specified Terms; Presence or Absence of Concomitant Medications
a. Information on concomitant medications is generally collected in two different ways, either by recording free text or using a pre-specified list of terms.
Since the solicitation of information on specific concomitant medications may affect the frequency at which they are reported, the fact that a specific
medication was solicited may be of interest to reviewers. CMPRESP and CMOCCUR are used together to indicate whether the intervention in CMTRT
was pre-specified and whether it occurred , respectively.
b. CMOCCUR is used to indicate whether a pre-specified medication was used. A value of Y indicates that the medication was used and N indicates that it was not.

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CDISC SDTM Implementation Guide (Version 3.1.2)
c.

If a medication was not pre-specified the value of CMOCCUR should be null. CMPRESP and CMOCCUR is a permissible fields and may be omitted
from the dataset if all medications were collected as free text. Values of CMOCCUR may also be null for pre-specified medications if no Y/N response
was collected; in this case, CMSTAT = NOT DONE, and CMREASND could be used to describe the reason the answer was missing.

Additional Timing Variables
a. CMSTRTPT, CMSTTPT, CMENRTPT and CMENTPT may be populated as necessary to indicate when a medication was used relative to specified time
points. For example, assume a subject uses birth control medication. The subject has used the same medication for many years and continues to do so.
The date the subject began using the medication (or at least a partial date) would be stored in CMSTDTC. CMENDTC is null since the end date is
unknown (it hasn‘t happened yet). This fact can be recorded by setting CMENTPT=‖2007-04-30‖ (the date the assessment was made) and
CMENRTPT=‖ONGOING‖.

Additional Permissible Interventions Qualifiers
a. Any additional Qualifiers from the Interventions Class may be added to this domain.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.1.1.2 EXAMPLES FOR CONCOMITANT MEDICATIONS DOMAIN MODEL
Example 1: Spontaneous concomitant medications with dosing information
Sponsors collect the timing of concomitant medication use with varying specificity, depending on the pattern of use; the type, purpose, and importance of the
medication; and the needs of the study. It is often unnecessary to record every unique instance of medication use, since the same information can be conveyed
with start and end dates and frequency of use. If appropriate, medications taken as needed (intermittently or sporadically over a time period) may be reported
with a start and end date and a frequency of ―PRN‖.
The example below shows three subjects who took the same medication on the same day.
Rows 1-6:
Rows 7-9:
Row 10:

Row
1
2
3
4
5
6
7
8
9
10

For the first subject (USUBJID=ABC-0001, each instance is recorded separately, and frequency (CMDOSFRQ) is ONCE.
For the second subject (USUBJID=ABC-0002, the second record (CMSEQ=2) shows that aspirin was taken twice on January 7 th, so the frequency
is BID. The frequency is also included for the other daily records to avoid confusion.
Records for the third subject are collapsed (this is shown as an example only, not as a recommendation) into a single entry that spans the relevant
time period, with a frequency of PRN. This approach assumes that knowing exactly when aspirin was used is not important for evaluating safety
and efficacy in this study.
STUDYID
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC

DOMAIN
CM
CM
CM
CM
CM
CM
CM
CM
CM
CM

USUBJID
ABC-0001
ABC-0001
ABC-0001
ABC-0001
ABC-0001
ABC-0001
ABC-0002
ABC-0002
ABC-0002
ABC-0003

CMSEQ
1
2
3
4
5
6
1
2
3
1

CMTRT
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN
ASPIRIN

CMDOSE
100
100
100
100
100
100
100
100
100
100

CMDOSU
MG
MG
MG
MG
MG
MG
MG
MG
MG
MG

CMDOSFRQ
ONCE
ONCE
ONCE
ONCE
ONCE
ONCE
Q24H
BID
Q24H
PRN

CMSTDTC
2004-01-01
2004-01-02
2004-01-03
2004-01-07
2004-01-07
2004-01-09
2004-01-01
2004-01-07
2004-01-09
2004-01-01

CMENDTC
2004-01-01
2004-01-02
2004-01-03
2004-01-07
2004-01-07
2004-01-09
2004-01-03
2004-01-07
2004-01-09
2004-01-09

Example 2: Spontaneous concomitant medications without dosing information
The example below is for a study that has a particular interest in whether subjects use any anticonvulsant medications. The medication history, dosing, etc. are
not of interest; the study only asks for the anticonvulsants to which subjects are being exposed.
Row
1
2

STUDYID
ABC123
ABC123

Page 80
November 12, 2008

DOMAIN
CM
CM

USUBJID
1
2

CMSEQ
1
1

CMTRT
LITHIUM
VPA

CMCAT
ANTI-CONVULSANT
ANTI-CONVULSANT

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 3: Pre-specified concomitant medications using CMPRESP, CMOCCUR, CMSTAT, and CMREASND
Sponsors often are interested in whether subjects are exposed to specific concomitant medications, and collect this information using a checklist. The example
below is for a study that has a particular interest in the antidepressant medications that subjects use. For the study‘s purposes, the absence is just as important as
the presence of a medication. This can be clearly shown by using CMOCCUR.
In this example, CMPRESP shows that the subjects were specifically asked if they use any of three antidepressants (Zoloft, Prozac, or Paxil). The value of
CMOCCUR indicates the response to the pre-specified medication question. CMSTAT indicates whether the response was missing for a pre-specified
medication, and CMREASND shows the reason for missing response. The medication details (e.g., dose, frequency) were not of interest in this study.
Row 1: Medication was solicited on CRF and was taken.
Row 2: Medication use solicited in CRF and was not taken.
Row 3: Medication use solicited in CRF but data was not collected.
Row
1
2
3

STUDYID
ABC123
ABC123
ABC123

DOMAIN
CM
CM
CM

USUBJID
1
1
1

CMSEQ
1
2
3

CMTRT
ZOLOFT
PROZAC
PAXIL

CMPRESP
Y
Y
Y

CMOCCUR
Y
N

CMSTAT

CMREASND

NOT DONE

Didn't ask due to interruption

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.1.2 EXPOSURE — EX
ex.xpt, Exposure — Interventions, Version 3.1.2. One record per constant dosing interval per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char EX
Identifier Two-character abbreviation for the domain.
18H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
492H

493H

USUBJID

Unique Subject Identifier Char

EXSEQ

Sequence Number

Num

EXGRPID

Group ID

Char

EXSPID

Sponsor-Defined Identifier Char

EXTRT

Name of Actual Treatment Char

EXCAT

Category for Treatment

EXSCAT

Subcategory for Treatment Char *

EXDOSE

Dose per Administration

Num

EXDOSTXT

Dose Description

Char

EXDOSU

Dose Units

Char (UNIT)

EXDOSFRM Dose Form

Char (FRM)

EXDOSFRQ

Dosing Frequency per
Interval

Char (FREQ)

EXDOSTOT

Total Daily Dose

Num

EXDOSRGM Intended Dose Regimen

Page 82
November 12, 2008

Char *

49H

189H

502H

Char

Identifier Identifier used to uniquely identify a subject across all studies for all
Req
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a Req
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain for a
Perm
subject.
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm
explicit line identifier or defined in the sponsor‘s operational database.
Example: Line number on a CRF Page.
Topic
Name of the intervention treatment — usually the verbatim name of the
Req
investigational treatment given during the dosing period for the observation.
Grouping Used to define a category of related records. Example: COMPARATOR Perm
Qualifier CLASS.
Grouping A further categorization of treatment.
Perm
Qualifier
Record
Amount of EXTRT administered or given.
Exp
Qualifier
Record
Dosing amounts or a range of dosing information collected in text form. Perm
Qualifier Example: 200-400.
Variable Units for EXDOSE and EXDOSTOT. Examples: ng, mg, or mg/kg.
Exp
Qualifier
Record
Dose form for EXTRT. Examples: TABLET, LOTION.
Exp
Qualifier
Variable Usually expressed as the number of repeated administrations of EXDOSE Perm
Qualifier within a specific time period. Examples: BID (twice daily), Q4S (once
every four weeks), BIS (twice a week).
Record
Total daily dose of EXTRT using the units in EXDOSU. Total dose over a Perm
Qualifier period other than day could be recorded in a separate Supplemental
Qualifier variable.
Variable Text description of the (intended) schedule or regimen for the
Perm
Qualifier Intervention. Examples: TWO WEEKS ON, TWO WEEKS OFF.
49H5

SDTM 2.2.4
SDTMIG 4.1.2.6
SDTM 2.2.4
496H

SDTM 2.2.1
SDTM 2.2.1,
SDTMIG 4.1.2.6
SDTM 2.2.1,
SDTMIG 4.1.2.6
SDTM 2.2.1
497H

498H

SDTM 2.2.1
SDTM 2.2.1,
SDTMIG 4.1.3.2
SDTM 2.2.1
50H1

SDTM 2.2.1

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name

Variable Label

EXROUTE

Route of Administration

EXLOT

Lot Number

EXLOC

Location of Dose
Administration
Treatment Vehicle

EXTRTV
EXVAMT
EXVAMTU
EXADJ
TAETORD
EPOCH

Treatment Vehicle
Amount
Treatment Vehicle
Amount Units
Reason for Dose
Adjustment
Order of Element within
Arm
Epoch

Controlled
Type Terms, Codelist
Role
or Format
Char (ROUTE)
Variable
Qualifier
Char
Record
Qualifier
Char (LOC)
Record
Qualifier
Char *
Record
Qualifier
Num
Variable
Qualifier
Char (UNIT)
Variable
Qualifier
Char *
Record
Qualifier
Num
Timing
1890H

503H

504H

Core

References

Route of administration for EXTRT. Examples: ORAL,
INTRAVENOUS.
Lot Number of the EXTRT product.

Perm

SDTM 2.2.1

Perm

SDTM 2.2.1

Specifies location of administration. Example: LEFT ARM for a topical
application.
Describes vehicle used for treatment. Example: SALINE.

Perm

SDTM 2.2.1

Perm

SDTM 2.2.1

Amount administered of the treatment vehicle indicated by EXTRTV

Perm

SDTM 2.2.1

Units of the treatment vehicle amount indicated by EXVAMT

Perm

SDTM 2.2.1

Describes reason or explanation of why a dose is adjusted – used only
when an adjustment is represented in EX.
Number that gives the order of the Element within the Arm.

Perm

SDTM 2.2.1

Perm

Trial Epoch of the Exposure record. Examples: SCREENING,
TREATMENT PHASE, FOLLOW-UP
The time when administration of the treatment indicated by EXTRT and
EXDOSE began.

Perm

The time when administration of the treatment indicated by EXTRT and
EXDOSE ended.

Perm

SDTM 2.2.5,
SDTMIG 5.3.1
SDTM 2.2.5,
SDTMIG 7.1.2
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.10
50H

Char *

Timing

Start Date/Time of
Treatment

Char ISO 8601

Timing

End Date/Time of
Treatment

Char ISO 8601

Study Day of Start of
Treatment

Num

Study Day of End of
Treatment

Num

EXDUR

Duration of Treatment

Char ISO 8601

EXTPT

Planned Time Point Name Char

EXSTDTC

CDISC Notes

506H

Exp
507H

508H

EXENDTC

Timing

509H

510H

EXSTDY

Timing

Study day of start of treatment relative to the sponsor-defined RFSTDTC. Perm
51H

512H

EXENDY

Timing

Study day of end of treatment relative to the sponsor-defined RFSTDTC. Perm
513H

514H

EXTPTNUM Planned Time Point
Number

Num

Timing
Timing

Timing

Collected duration and unit of a treatment. Used only if collected on the Perm
CRF and not derived from start and end date/times.
1. Text Description of time when a dose should be given.
Perm
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See EXTPTNUM and EXTPTREF.
Examples: Start or 5 min post.
Numerical version of EXTPT to aid in sorting.
Perm
51H

516H7

SDTM 2.2.5,
SDTMIG 4.1.4.10
518H9

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
EXELTM

Planned Elapsed Time
from Time Point Ref

EXTPTREF


Variable Label

Time Point Reference

Controlled
Type Terms, Codelist
Role
or Format
Char ISO 8601
Timing

Char

Timing

CDISC Notes

Core

Planned elapsed time (in ISO 8601 format) relative to the planned fixed Perm
reference (EXTPTREF). This variable is useful where there are repetitive
measures. Not a clock time. Represented as an ISO duration.
Name of the fixed reference point referred to by EXELTM, EXTPTNUM, Perm
and EXTPT. Examples: PREVIOUS DOSE, PREVIOUS MEAL.

References
SDTM 2.2.5,
SDTMIG 4.1.4.10
520H1

SDTM 2.2.5,
SDTMIG 4.1.4.10
52H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.1.2.1 ASSUMPTIONS FOR EXPOSURE DOMAIN MODEL

EX Definition
a. The Exposure domain model records the details of a subject‘s exposure to protocol-specified study treatment. Study treatment may be any intervention
that is prospectively defined as a test material within a study, and is typically but not always supplied to the subject. Examples include but are not
limited to placebo, active comparators, and investigational products. Treatments that are not protocol-specified should be recorded in the Concomitant
Medication (CM) domain.
b. This domain should contain one record per constant dosing interval per subject. "Constant dosing interval" is sponsor-defined, and may include any
period of time that can be described in terms of a known treatment given at a consistent dose and frequency. For example, for a study with once-a-week
administration of a standard dose for 6 weeks, exposure may be represented as one of the following:
 If information about each dose is not collected, there would be a single record per subject, spanning the entire treatment phase
 If the sponsor monitors each treatment administration and deviations in treatment or dose occur, there could be up to six records (one for
each weekly administration).

The Exposure domain is required for all studies that include investigational product. Exposure information may be directly or indirectly determined.
Regardless of how it is known, it must be represented using the Exposure domain, and the metadata should explain how it was populated. Common
methods for determining exposure (from most direct to least direct) include the following:
1) Actual observation of the administration of drug by the investigator
2) Automated dispensing device which records administrations
3) Subject recall (e.g., via diary)
4) Derived from drug accountability data (e.g., pill counts)
5) Derived from the protocol

Categorization and Grouping
a. EXCAT and EXSCAT may be used when appropriate to categorize treatments into categories and subcategories. For example, if a study contains
several active comparator medications, EXCAT may be set to ―ACTIVE COMPARATOR.‖ Such categorization may not be useful in most studies, so
these variables are permissible but not expected.

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CDISC SDTM Implementation Guide (Version 3.1.2)
3.

Exposure Treatment Description
a. EXTRT captures the name of the investigational treatment and it is the topic variable. It is a required variable and must have a value. EXTRT should
only include the treatment name and should not include dosage, formulation or other qualifying information. For example, ―ASPIRIN 100MG
TABLET‖ is not a valid value for EXTRT. This example should be expressed as EXTRT= ―ASPIRIN‖, EXDOSE= ―100‖, EXDOSU= ―mg‖, and
EXDOSFRM= ―TABLET‖.
b. Doses of placebo should be represented as per Exposure Example 5 below.

Timing Variables
a. The timing of exposure to study treatment is captured by the start/end date and start/end time of each constant dosing interval. If the subject is only
exposed to study medication within a clinical encounter (e.g., if an injection is administered at the clinic), VISITNUM may be added to the domain as
an additional timing variable. VISITDY and VISIT would then also be permissible Qualifiers. However if the beginning and end of a constant dosing
interval is not confined within the time limits of a clinical encounter (e.g., if a subjects takes pills at home), then it is not appropriate to include
VISITNUM in the EX domain. This is because EX is designed to capture the timing of exposure to treatment, not the timing of dispensing treatment.
Furthermore, VISITNUM should not be used to indicate that treatment began at a particular visit and continued for a period of time. The SDTM does
not have any provision for recording "start visit" and "end visit" since such information is redundant with start date/time and end date/time.

Additional Interventions Qualifiers
b. The variables --PRESP, --OCCUR, --STAT, and --REASND from the Interventions general observation class would not generally be used in the EX
domain because EX should only contain medications received.
c. Other additional Qualifiers from the SDTM Interventions Class may be added to this domain.

6.1.2.2 EXAMPLES FOR EXPOSURE DOMAIN MODEL
Example 1:
This is an example of an Exposure dataset for a parallel-design study. In this example, subjects were randomized to one of three treatment groups: Drug A 40 mg
Q24H, Drug A 20 mg Q24H, or Drug B 150 mg BID. Drug C was assigned as supplemental therapy for the three groups. The study included 8 weeks of treatment,
with subjects remaining on the same treatment throughout the study. With respect to timing of doses, the sponsor only collected the start and stop dates of
uninterrupted periods of treatment. Note below that Subject 12345003 missed taking study medications on Study Days 23 and 24.
Row STUDYID DOMAIN USUBJID EXSEQ EXTRT EXDOSE EXDOSU
12345
EX
12345001
1
DRUG A
40
mg
1
12345
EX
12345001
2
DRUG C
30
mg
2
12345
EX
12345002
1
DRUG A
20
mg
3
12345
EX
12345002
2
DRUG C
30
mg
4
12345
EX
12345003
1
DRUG C
30
mg
5
12345
EX
12345003
2
DRUG B
150
mg
6
12345
EX
12345003
3
DRUG C
30
mg
7
12345
EX
12345003
4
DRUG B
150
mg
8

EXDOSFRM
TABLET
CAPSULE
TABLET
CAPSULE
CAPSULE
TABLET
CAPSULE
TABLET

EXDOSFRQ EXDOSTOT EXROUTE EXSTDTC EXENDTC EXSTDY EXENDY
Q24H
40
ORAL
2002-01-10 2002-03-08
1
58
BID
60
ORAL
2002-01-10 2002-03-08
1
58
Q24H
20
ORAL
2002-01-10 2002-03-07
1
57
BID
60
ORAL
2002-01-10 2002-03-07
1
57
BID
60
ORAL
2002-01-11 2002-02-01
1
22
BID
300
ORAL
2002-01-11 2002-02-01
1
22
BID
60
ORAL
2002-02-04 2002-03-06
25
55
BID
300
ORAL
2002-02-04 2002-03-06
25
55

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 2:
This is an example of an Exposure dataset for a single crossover study comparing once daily oral administration of Drug A 20 mg capsules with Drug B 30 mg
coated tablets. Study drug was taken for 3 consecutive mornings 30 minutes prior to a standardized breakfast. There was a 6-day washout period between
treatments.

Row STUDYID DOMAIN USUBJID EXSEQ EXGRPID EXTRT EXDOSE EXDOSU
56789
EX
56789001
1
1
DRUG A
20
mg
1

EXDOSFRM
CAPSULE

EXDOSFRQ
Q24H

56789

56789001

DRUG A

CAPSULE

Q24H

56789

56789001

DRUG A

CAPSULE

Q24H

56789

56789001

DRUG B

TABLET, COATED

Q24H

56789

56789001

DRUG B

TABLET, COATED

Q24H

56789

56789001

DRUG B

TABLET, COATED

Q24H

56789

56789003

DRUG B

TABLET, COATED

Q24H

56789

56789003

DRUG B

TABLET, COATED

Q24H

56789

56789003

DRUG B

TABLET, COATED

Q24H

56789

56789003

DRUG A

CAPSULE

Q24H

56789

56789003

DRUG A

CAPSULE

Q24H

56789

56789003

DRUG A

CAPSULE

Q24H

Row
1 (cont)

EXDOSTOT EXROUTE

EXTPT

ORAL

EXSTDTC
EXENDTC
EXSTDY EXENDY
2002-07-01T07:30 2002-07-01T07:30
1
1

2 (cont)

ORAL

2002-07-02T07:30 2002-07-02T07:30

30 MINUTES PRIOR

STD BREAKFAST

3 (cont)

ORAL

2002-07-03T07:32 2002-07-03T07:32

30 MINUTES PRIOR

STD BREAKFAST

4 (cont)

ORAL

2002-07-09T07:30 2002-07-09T07:30

30 MINUTES PRIOR

STD BREAKFAST

5 (cont)

ORAL

2002-07-10T07:30 2002-07-10T07:30

30 MINUTES PRIOR

STD BREAKFAST

6 (cont)

ORAL

2002-07-11T07:34 2002-07-11T07:34

30 MINUTES PRIOR

STD BREAKFAST

7 (cont)

ORAL

2002-07-03T07:30 2002-07-03T07:30

30 MINUTES PRIOR

STD BREAKFAST

8 (cont)

ORAL

2002-07-04T07:24 2002-07-04T07:24

30 MINUTES PRIOR

STD BREAKFAST

9 (cont)

ORAL

2002-07-05T07:24 2002-07-05T07:24

30 MINUTES PRIOR

STD BREAKFAST

10 (cont)

ORAL

2002-07-11T07:30 2002-07-11T07:30

30 MINUTES PRIOR

STD BREAKFAST

11 (cont)

ORAL

2002-07-12T07:43 2002-07-12T07:43

30 MINUTES PRIOR

STD BREAKFAST

12 (cont)

ORAL

2002-07-13T07:38 2002-07-13T07:38

30 MINUTES PRIOR

STD BREAKFAST

Page 86
November 12, 2008

30 MINUTES PRIOR

EXTPTREF
STD BREAKFAST

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 3:
This is an example of an Exposure dataset for an open-label study examining the tolerability of different doses of Drug A. Study drug was taken daily for three
months. Dose adjustments were allowed as needed in response to tolerability or efficacy issues.
Row STUDYID DOMAIN USUBJID EXSEQ EXTRT EXDOSE EXDOSU EXDOSFRM
37841
EX
37841001
1
DRUG A
20
mg
TABLET
1

EXADJ

EXSTDTC EXENDTC
2002-07-01 2002-10-01

37841

37841002

DRUG A

TABLET

2002-04-02 2002-04-21

37841

37841002

DRUG A

TABLET

Reduced due to toxicity 2002-04-22 2002-07-01

37841

37841003

DRUG A

TABLET

37841

37841003

DRUG A

TABLET

37841

37841003

DRUG A

TABLET

2002-05-09 2002-06-01
Increased due to
suboptimal efficacy
Increased due to
suboptimal efficacy

2002-06-02 2002-07-01
2002-07-02 2002-08-01

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CDISC SDTM Implementation Guide (Version 3.1.2)
Example 4:
This is an example of a titration Exposure dataset for a study that gradually increases dosage while simultaneously evaluating efficacy and toleration of the
treatment regimen. The schedule specifies that Drug A be administered twice daily starting with 100 mg for 3 days, then increase to 200 mg daily for 3 days, then
increase further in 100-mg increments every three days until signs of intolerance are noted or no improvement in efficacy is observed.
Row STUDYID DOMAIN USUBJID EXSEQ EXGRPID EXTRT EXDOSE EXDOSU
70912
EX
23301996
1
1
DRUG A
100
mg
1
23301
EX
23301996
2
1
DRUG A
100
mg
2
23301
EX
23301996
3
1
DRUG A
100
mg
3
23301
EX
23301996
4
2
DRUG A
200
mg
4
23301
EX
23301996
5
2
DRUG A
200
mg
5
23301
EX
23301996
6
2
DRUG A
200
mg
6
23301
EX
23301996
7
1
DRUG A
300
mg
7
23301
EX
23301996
8
1
DRUG A
300
mg
8
23301
EX
23301996
9
1
DRUG A
300
mg
9
23301
EX
23301996
10
2
DRUG A
400
mg
10
23301
EX
23301996
11
2
DRUG A
400
mg
11
23301
EX
23301996
12
2
DRUG A
400
mg
12

EXDOSFRM EXDOSFRQ
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID
CAPSULE
BID

Row EXDOSTOT EXROUTE
EXSTDTC
EXENDTC
EXSTDY EXENDY
200
ORAL
2004-07-01T07:30 2004-07-01T07:30
1
1
1 (cont)
200
ORAL
2004-07-02T07:30 2004-07-02T07:30
2
2
2 (cont)
200
ORAL
2004-07-03T07:32 2004-07-03T07:32
3
3
3 (cont)
400
ORAL
2004-07-09T07:30 2004-07-09T07:30
9
9
4 (cont)
400
ORAL
2004-07-10T07:30 2004-07-10T07:30
10
10
5 (cont)
400
ORAL
2004-07-11T07:34 2004-07-11T07:34
11
11
6 (cont)
600
ORAL
2004-07-01T07:30 2004-07-01T07:30
1
1
7 (cont)
600
ORAL
2004-07-02T07:30 2004-07-02T07:30
2
2
8 (cont)
600
ORAL
2004-07-03T07:32 2004-07-03T07:32
3
3
9 (cont)
800
ORAL
2004-07-09T07:30 2004-07-09T07:30
9
9
10 (cont)
800
ORAL
2004-07-10T07:30 2004-07-10T07:30
10
10
11 (cont)
800
ORAL
2004-07-11T07:34 2004-07-11T07:34
11
11
12 (cont)

Example 5:
The table below presents data for a study comparing low dose aspirin to placebo. Two rows are shown: one for a subject receiving active study drug and one for a
subject receiving placebo.
USUBJID
2008-039-001
2008-039-002
Page 88
November 12, 2008

EXSEQ
1
1

EXTRT
Aspirin
Placebo

EXDOSE
81
0

EXDOSU
mg
mg

EXDOSEFRM
TABLET
TABLET

EXDOSFRQ
QD
QD

CDISC SDTM Implementation Guide (Version 3.1.2)

6.1.3 SUBSTANCE USE — SU
su.xpt, Substance Use — Interventions, Version 3.1.2. One record per substance type per reported occurrence per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label

Study Identifier
Domain Abbreviation

Type

Controlled
Terms or
Format

Char
Char SU
189H

Role

CDISC Notes

Identifier Unique identifier for a study.
Identifier Two-character abbreviation for the domain.

Core

References

Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
523H

524H

USUBJID

Unique Subject Identifier

Char

SUSEQ

Sequence Number

Num

SUGRPID

Group ID

Char

SUSPID

Sponsor-Defined Identifier Char

SUTRT

Reported Name of
Char
Substance
SUMODIFY Modified Substance Name Char
SUDECOD

SUCAT
SUSCAT
SUPRESP

Standardized Substance
Name

Char *

Category for Substance
Char *
Use
Subcategory for Substance Char *
Use
SU Pre-Specified
Char (NY)
1892H

Req
52H6

Req
Perm
Perm

SDTM 2.2.4
SDTMIG 4.1.2.6
SDTM 2.2.4

Req

SDTM 2.2.1

Synonym If SUTRT is modified, then the modified text is placed here.
Qualifier
Synonym Standardized or dictionary-derived text description of SUTRT or
Qualifier SUMODIFY if the sponsor chooses to code the substance use. The
sponsor is expected to provide the dictionary name and version used to
map the terms utilizing the define.xml external codelist attributes.
Grouping Used to define a category of related records. Examples: TOBACCO,
Qualifier ALCOHOL, or CAFFEINE.
Grouping A further categorization of substance use. Examples: CIGARS,
Qualifier CIGARETTES, BEER, WINE
Record
Used to indicate whether (Y/null) information about the use of a specific
Qualifier substance was solicited on the CRF.

Perm

SDTM 2.2.1,
SDTMIG 4.1.3.6
SDTM 2.2.1,
SDTMIG 4.1.3.6

Record
Qualifier

Perm

527H

528H

Perm
529H

Perm

SDTM 2.2.1,
SDTMIG 4.1.2.6
SDTM 2.2.1,
SDTMIG 4.1.2.6
SDTM 2.2.1,
SDTMIG 4.1.2.7.3,
SDTMIG 4.1.5.7
SDTM 2.2.1,
SDTMIG 4.1.5.7
530H

Perm
531H

Perm
532H

53H

SUOCCUR

SU Occurrence

Char (NY)
1893H

When the use of specific substances is solicited, SUOCCUR is used to
indicate whether or not (Y/N) a particular pre-specified substance was
used. Values are null for substances not specifically solicited.

534H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
SUSTAT

Variable Label

Completion Status

Type

Controlled
Terms or
Format
Char (ND)
1894H

Role

Record
Qualifier

SUREASND Reason Substance Use Not Char
Collected

Record
Qualifier

SUCLAS

Variable
Qualifier

CDISC Notes

Core

When the use of pre-specified substances is solicited, the completion status
Perm
indicates that there was no response to the question about the pre-specified
substance. When there is no pre-specified list on the CRF, then the completion
status indicates that substance use was not assessed for the subject.
Describes the reason substance use was not collected. Used in conjunction Perm
with SUSTAT when value of SUSTAT is NOT DONE.

References

SDTM 2.2.1,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
53H

536H

SDTM 2.2.1,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.1,
SDTMIG 4.1.3.5
537H

538H

Substance Use Class

Char *

Substance use class. May be obtained from coding. When coding to a
Perm
single class, populate with class value. If using a dictionary and coding to
multiple classes, then follow assumption 4.1.2.8.3 or omit SUCLAS.
Code corresponding to SUCLAS. May be obtained from coding.
Perm
539H

SUCLASCD Substance Use Class Code Char *
SUDOSE

Substance Use
Consumption
SUDOSTXT Substance Use
Consumption Text
SUDOSU
Consumption Units

Num

SUDOSFRM Dose Form

Char *

Char
Char (UNIT)
547H

SUDOSFRQ Use Frequency Per Interval Char (FREQ)
549H

SUDOSTOT Total Daily Consumption

Num

SUROUTE

Route of Administration

Char (ROUTE)

SUSTDTC

Start Date/Time of
Substance Use

Char ISO 8601

End Date/Time of
Substance Use

Char ISO 8601

Study Day of Start of
Substance Use

Num

Study Day of End of
Substance Use

Num

1895H

Variable
Qualifier
Record
Qualifier
Record
Qualifier
Variable
Qualifier
Record
Qualifier
Variable
Qualifier
Record
Qualifier
Variable
Qualifier
Timing

540H123

46H

Amount of SUTRT consumed.

Perm

SDTM 2.2.1,
SDTMIG 4.1.3.5
SDTM 2.2.1

Substance use consumption amounts or a range of consumption
information collected in text form.
Units for SUDOSE, SUDOSTXT, and SUDOSTOT. Examples:
OUNCES, CIGARETTE EQUIVALENTS, or GRAMS.
Dose form for SUTRT. Examples: INJECTABLE, LIQUID, or
POWDER.
Usually expressed as the number of repeated administrations of SUDOSE
within a specific time period. Example: Q24H (every day)
Total daily use of SUTRT using the units in SUDOSU. If sponsor needs to
aggregate the data over a period other than daily, then the aggregated total
could be recorded in a Supplemental Qualifier variable.
Route of administration for SUTRT. Examples: ORAL,
INTRAVENOUS.

Perm

SDTM 2.2.1

Perm
Perm

SDTM 2.2.1,
SDTMIG 4.1.3.2
SDTM 2.2.1

Perm

SDTM 2.2.1

Perm

SDTM 2.2.1

Perm

SDTM 2.2.1

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6

54H6

548H

50H

51H

SUENDTC

Timing

Perm
52H

53H

SUSTDY

Timing

Study day of start of substance use relative to the sponsor-defined
RFSTDTC.

Perm

Study day of end of substance use relative to the sponsor-defined
RFSTDTC.

Perm

54H

SUENDY

Timing

56H

57H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
SUDUR
SUSTRF

SUENRF

SUSTRTPT

SUSTTPT

Variable Label

Type

Controlled
Terms or
Format
Duration of Substance Use Char ISO 8601

Role

Timing

Start Relative to Reference Char (STENRF)
Period

Timing

End Relative to Reference Char (STENRF)
Period

Timing

1896H

1897H

Start Relative to Reference Char BEFORE,
Timing
Time Point
COINCIDENT,
AFTER, U
Start Reference Time Point Char
Timing

SUENRTPT End Relative to Reference Char BEFORE,
Timing
Time Point
COINCIDENT,
AFTER,
ONGOING, U
SUENTPT
End Reference Time Point Char
Timing

CDISC Notes

Collected duration of substance use in ISO 8601 format. Used only if
collected on the CRF and not derived from start and end date/times.
Describes the start of the substance use relative to the sponsor-defined
reference period. The sponsor-defined reference period is a continuous
period of time defined by a discrete starting point and a discrete ending
point (represented by RFSTDTC and RFENDTC in Demographics). If
information such as "PRIOR", "ONGOING", or "CONTINUING" was
collected, this information may be translated into SUSTRF.
Describes the end of the substance use with relative to the sponsordefined reference period. The sponsor-defined reference period is a
continuous period of time defined by a discrete starting point and a
discrete ending point (represented by RFSTDTC and RFENDTC in
Demographics). If information such as "PRIOR", "ONGOING", or
"CONTINUING" was collected, this information may be translated into
SUENRF.
Identifies the start of the substance as being before or after the reference
time point defined by variable SUSTTPT.

Core

References

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTM 2.2.5,
SDTMIG 4.1.4.7
58H

Perm
59H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.7
560H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.7
561H

Description or date/time in ISO 8601 character format of the reference
Perm
point referred to by SUSTRTPT. Examples: "2003-12-15" or "VISIT 1".
Identifies the end of the substance as being before or after the reference Perm
time point defined by variable SUENTPT.

SDTM 2.2.5,
SDTMIG 4.1.4.7
SDTM 2.2.5,
SDTMIG 4.1.4.7

Description or date/time in ISO 8601 character format of the reference
Perm
point referred to by SUENRTPT. Examples: "2003-12-25" or "VISIT 2".

SDTM 2.2.5,
SDTMIG 4.1.4.7

562H

563H

564H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.1.3.1 ASSUMPTIONS FOR SUBSTANCE USE DOMAIN MODEL
1.

The intent of the domain is to capture substance use information that may be used to assess the efficacy and/or safety of therapies that look to mitigate the
effects of chronic substance use, or that could be used as covariates in other efficacy and/or safety analyses.

SU Definition
a. This information may be independent of planned study evaluations, or may be a key outcome (e.g., planned evaluation) of a clinical trial.
b. In many clinical trials, detailed substance use information as provided for in the domain model above may not be required (e.g., the only information
collected may be a response to the question ―Have you ever smoked tobacco?‖); in such cases, many of the Qualifier variables would not be submitted.
c. SU may contain responses to questions about use of pre-specified substances as well as records of substance use collected as free text.

Substance Use Description and Coding
a. SUTRT captures the verbatim or the pre-specified text collected for the substance. It is the topic variable for the SU dataset. SUTRT is a required
variable and must have a value.
b. SUMODIFY is a permissible variable and should be included if coding is performed and the sponsor‘s procedure permits modification of a verbatim
substance use term for coding. The modified term is listed in SUMODIFY. The variable may be populated as per the sponsor‘s procedures.
c. SUDECOD is the preferred term derived by the sponsor from the coding dictionary if coding is performed. It is a permissible variable. Where deemed
necessary by the sponsor, the verbatim term (SUTRT) should be coded using a standard dictionary such as WHO Drug. The sponsor is expected to
provide the dictionary name and version used to map the terms utilizing the define.xml external codelist attributes.

Additional Categorization and Grouping
a. SUCAT and SUSCAT should not be redundant with the domain code or dictionary classification provided by SUDECOD, or with SUTRT. That is,
they should provide a different means of defining or classifying SU records. For example, a sponsor may be interested in identifying all substances that
the investigator feels might represent opium use, and to collect such use on a separate CRF page. This categorization might differ from the
categorization derived from the coding dictionary.
b. SUGRPID may be used to link (or associate) different records together to form a block of related records within SU at the subject level (see Section
4.1.2.6). It should not be used in place of SUCAT or SUSCAT.
56H

Timing Variables
a. SUSTDTC and SUENDTC may be populated as required.
b. If substance use information is collected more than once within the CRF (indicating that the data are visit-based) then VISITNUM would be added to
the domain as an additional timing variable. VISITDY and VISIT would then be permissible variables.

Additional Permissible Interventions Qualifiers
a. Any additional Qualifiers from the Interventions Class may be added to this domain.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.1.3.2 EXAMPLE FOR SUBSTANCE USE DOMAIN MODEL
The example below illustrates how typical substance use data could be populated. Here, the CRF collected smoking data (smoking status: previous, current,
never; if a current or past smoker, how many packs per day; if a former smoker, what year did the subject quit) and current caffeine use (what caffeine drinks
have been consumed today; how many cups today). SUCAT allows the records to be grouped into smoking-related data and caffeine-related data. In this
example, the treatments are pre-specified on the CRF page so SUTRT does not require a standardized SUDECOD equivalent.
Subject 1234005 is a 2-pack/day current smoker. ―Current‖ implies that smoking started sometime before the time the question was asked
(SUSTTPT = 2006-01-01, SUSTRTPT = BEFORE) and will end sometime after that date (SUENRTPT = ONGOING). See Section 4.1.4.7 for the
use of these variables. Both the beginning and ending reference time points for this question are the date of the assessment.
Row 2:
The same subject drank three cups of coffee on the day of the assessment.
Row 3:
Subject 1234006 is a former smoker. The date the subject began smoking is unknown but we know that it was sometime before the assessment date.
This is shown by the values of SUSTTPT and SUSTRTPT (taken from the timing variables for all classes). The end date of smoking was collected
so SUENTPT and SUENRTPT are not populated. Instead, the end date is in SUENDTC.
Rows 4-5: The same subject drank tea (Row 4) and coffee (Row 5) on the day of the assessment.
Row 6:
Subject 1234007 has missing data for the smoking questions. This is indicated by SUSTAT=NOT DONE. The reason is in SUREASND.
Row 7:
The same subject also had missing data for all of the caffeine questions.
Not shown: Subject 1234008 has never smoked, so does not have a tobacco record. Alternatively, a row for the subject could have been included with
SUOCCUR=N and not populating the dosing and timing fields; the interpretation would be the same. The subject did not drink any caffeinated
drinks on the day of the assessment so does not have any caffeine records. Therefore this subject does not appear in the data.

Row 1:

56H

Row
1
2
3
4
5

STUDYID
1234
1234
1234
1234
1234

DOMAIN
SU
SU
SU
SU
SU

USUBJID
1234005
1234005
1234006
1234006
1234006

SUSEQ
1
2
1
2
3

SUTRT
CIGARETTES
COFFEE
CIGARETTES
TEA
COFFEE

SUCAT
TOBACCO
CAFFEINE
TOBACCO
CAFFEINE
CAFFEINE

1234

1234007

CIGARETTES

TOBACCO

1234

1234007

CAFFEINE

Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)

SUSTAT

SUREASND

NOT
DONE
NOT
DONE

Subject left office before CRF
was completed
Subject left office before CRF
was completed

SUDOSE
2
3
1
1
2

SUDOSU
PACK
CUP
PACK
CUP
CUP

SUDOSFRQ
PER DAY
PER DAY
PER DAY
PER DAY
PER DAY

SUSTDTC SUENDTC SUSTTPT SUSTRTPT SUENTPT SUENRTPT
2006-01-01
BEFORE
2006-01-01 ONGOING
2006-01-01 2006-01-01
2003
2006-03-15
BEFORE
2006-03-15 2006-03-15
2006-03-15 2006-03-15

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.2 EVENTS
6.2.1 ADVERSE EVENTS — AE
ae.xpt, Adverse Events — Events, Version 3.1.2,. One record per adverse event per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled Terms,
Codelist or Format

Type

Char
Char AE
189H

Role

CDISC Notes

Identifier Unique identifier for a study.
Identifier Two-character abbreviation for the domain.

Core
Req
Req

References

Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4
SDTMIG 4.1.2.3
SDTM 2.2.4

Perm
Perm

SDTM 2.2.4
SDTM 2.2.4

Perm

SDTM 2.2.4

Req

SDTM 2.2.2,
SDTMIG 4.1.3.6
SDTM 2.2.2,
SDTMIG 4.1.3.6
SDTM 2.2.2,
SDTMIG 4.1.3.5
SDTMIG 4.1.3.6

567H

568H

USUBJID

Unique Subject Identifier Char

AESEQ

Sequence Number

Num

AEGRPID
AEREFID

Group ID
Reference ID

Char
Char

AESPID

Sponsor-Defined
Identifier

Char

AETERM

Reported Term for the
Char
Adverse Event
AEMODIFY Modified Reported Term Char
AEDECOD

Req

Synonym
Qualifier
Synonym
Qualifier

Perm

569H70

571H

Dictionary-Derived Term Char *

If AETERM is modified to facilitate coding, then AEMODIFY will
contain the modified text.
Dictionary-derived text description of AETERM or AEMODIFY.
Equivalent to the Preferred Term (PT in MedDRA). The sponsor is

572H

Req
573H4

expected to provide the dictionary name and version used to map
the terms utilizing the define.xml external codelist attributes
AECAT
AESCAT
AEPRESP

Category for Adverse
Char *
Event
Subcategory for Adverse Char *
Event
Pre-Specified Adverse
Char (NY)
Event

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November 12, 2008

189H

Grouping
Qualifier
Grouping
Qualifier
Record
Qualifier

Used to define a category of related records. Example: BLEEDING,
NEUROPSYCHIATRIC.
A further categorization of adverse event. Example: NEUROLOGIC.

576H

Perm

SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.7
SDTMIG 4.1.5.7
57H

Perm
578H

A value of ―Y‖ indicates that this adverse event was pre-specified on the Perm
CRF. Values are null for spontaneously reported events (i.e., those
collected as free-text verbatim terms)
579H

580H

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Variable Label
Name
AEBODSYS Body System or Organ
Class

Controlled Terms,
Role
Codelist or Format
Char *
Record
Qualifier

AELOC

Location of Event

Char (LOC)

AESEV

Severity/Intensity

Char (AESEV)

AESER

Serious Event

Char (NY)

AEACN

Action Taken with Study Char (ACN)
Treatment

Type

584H

190H

1902H

Record
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier

AEACNOTH Other Action Taken

Char

Record
Qualifier

AEREL

Char *

Record
Qualifier

Causality

AERELNST Relationship to NonStudy Treatment

Char

Record
Qualifier

AEPATT

Pattern of Adverse Event Char *

AEOUT

Outcome of Adverse
Event
Involves Cancer

Char (OUT)

Congenital Anomaly or
Birth Defect
Persist or Signif
Disability/Incapacity

Char (NY)

AESCAN
AESCONG
AESDISAB

1903H

Char (NY)
1904H

1905H

Char (NY)
1906H

Record
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier

CDISC Notes
Dictionary derived. Body system or organ class used by the sponsor from
the coding dictionary (e.g., MedDRA). When using a multi-axial
dictionary such as MedDRA, this should contain the SOC used for the
sponsor‘s analyses and summary tables which may not necessarily be the
primary SOC.
Describes anatomical location relevant for the event (e.g., LEFT ARM for
skin rash).
The severity or intensity of the event. Examples: MILD, MODERATE,
SEVERE.
Is this a serious event?

Core
Exp

References
SDTM 2.2.2,
SDTMIG 4.1.3.5
581H23

Perm

SDTM 2.2.2

Perm

SDTM 2.2.2

Exp

SDTM 2.2.2

Describes changes to the study treatment as a result of the event. AEACN Exp
is specifically for the relationship to study treatment. AEACNOTH is for
actions unrelated to dose adjustments of study treatment. Examples of
AEACN values include ICH E2B values: DRUG WITHDRAWN, DOSE
REDUCED, DOSE INCREASED, DOSE NOT CHANGED,
UNKNOWN or NOT APPLICABLE
Describes other actions taken as a result of the event that are unrelated to Perm
dose adjustments of study treatment. Usually reported as free text.
Example: ―TREATMENT UNBLINDED. PRIMARY CARE
PHYSICIAN NOTIFIED.‖
Records the investigator's opinion as to the causality of the event Exp

SDTM 2.2.2

to the treatment. ICH E2A and E2B examples include NOT
RELATED, UNLIKELY RELATED, POSSIBLY RELATED,
RELATED. Controlled Terminology may be defined in the future.
Check with regulatory authority for population of this variable
Records the investigator's opinion as to whether the event may have been Perm
due to a treatment other than study drug. May be reported as free text.
Example: "MORE LIKELY RELATED TO ASPIRIN USE.‖.
Used to indicate the pattern of the event over time. Examples:
Perm
INTERMITTENT, CONTINUOUS, SINGLE EVENT.
Description of the outcome of an event.
Perm

SDTM 2.2.2

Was the serious event associated with the development of cancer?

Perm

SDTM 2.2.2

Was the serious event associated with congenital anomaly or birth defect? Perm

SDTM 2.2.2

Did the serious event result in persistent or significant
disability/incapacity?

SDTM 2.2.2

Perm

SDTM 2.2.2
SDTM 2.2.2

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CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
AESDTH
AESHOSP

Variable Label
Results in Death

AESLIFE

Requires or Prolongs
Hospitalization
Is Life Threatening

AESOD

Occurred with Overdose

AESMIE

Other Medically
Important Serious Event
AECONTRT Concomitant or
Additional Trtmnt Given
AETOXGR Standard Toxicity Grade

Controlled Terms,
Role
Codelist or Format
Char (NY)
Record
Qualifier
Char (NY)
Record
Qualifier
Char (NY)
Record
Qualifier
Char (NY)
Record
Qualifier
Char (NY)
Record
Qualifier
Char (NY)
Record
Qualifier
Char *
Record
Qualifier
Type

1907H

1908H

190H

19H

192H

CDISC Notes

Core

References

Did the serious event result in death?

Perm

SDTM 2.2.2

Did the serious event require or prolong hospitalization?

Perm

SDTM 2.2.2

Was the serious event life threatening?

Perm

SDTM 2.2.2

Did the serious event occur with an overdose?

Perm

SDTM 2.2.2

Do additional categories for seriousness apply?

Perm

SDTM 2.2.2

Was another treatment given because of the occurrence of the event?

Perm

SDTM 2.2.2

Toxicity grade according to a standard toxicity scale such as Common
Perm
Terminology Criteria for Adverse Events v3.0 (CTCAE). Sponsor should
specify name of the scale and version used in the metadata (see Section
6.2.1.1, Assumption 6d). If value is from a numeric scale, represent only
the number (e.g., ―2‖ and not ―Grade 2‖).
Exp

SDTM 2.2.2

58H

AESTDTC

Start Date/Time of
Adverse Event

Char ISO 8601

Timing

End Date/Time of
Adverse Event

Char ISO 8601

Study Day of Start of
Adverse Event
Study Day of End of
Adverse Event
Duration of Adverse
Event

Num

Timing

Num

Timing

Char ISO 8601

Timing

End Relative to
Reference Period

Char (STENRF)

SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.2
SDTM 2.2.5,
SDTMIG 4.1.4.1;
SDTMIG 4.1.4.2
SDTM 2.2.5,
SDTMIG 4.1.4.4
SDTM 2.2.5,
SDTMIG 4.1.4.4
SDTM 2.2.5,
SDTMIG 4.1.4.3
586H

587H

AEENDTC

Timing

Exp
58H

589H

AESTDY
AEENDY
AEDUR

AEENRF

AEENRTPT End Relative to
Reference Time Point
AEENTPT

End Reference Time
Point

Study day of start of adverse event relative to the sponsor-defined
RFSTDTC.
Study day of end of event relative to the sponsor-defined RFSTDTC.

Perm
590H

Perm
591H

Timing

Char BEFORE, AFTER, Timing
COINCIDENT,
ONGOING, U
Char
Timing

Collected duration and unit of an adverse event. Used only if collected on Perm
the CRF and not derived from start and end date/times. Example:
P1DT2H (for 1 day, 2 hours).
Describes the end of the event relative to the sponsor-defined reference
Perm
period. The sponsor-defined reference period is a continuous period of
time defined by a discrete starting point (RFSTDTC) and a discrete
ending point (RFENDTC) of the trial.
Identifies the end of the event as being before or after the reference time Perm
point defined by variable AEENTPT.
Description of date/time in ISO 8601 character format of the reference
Perm
point referred to by AEENRTPT. Examples: "2003-12-25" or "VISIT 2".

592H

SDTM 2.2.5,
SDTMIG 4.1.4.7
593H

SDTM 2.2.5,
SDTMIG 4.1.4.7
594H

SDTM 2.2.5,
SDTMIG 4.1.4.7
59H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
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CDISC SDTM Implementation Guide (Version 3.1.2)

6.2.1.1 ASSUMPTIONS FOR ADVERSE EVENT DOMAIN MODEL
1.

AE Definition
The Adverse Events dataset includes clinical data describing "any untoward medical occurrence in a patient or clinical investigation subject administered a
pharmaceutical product and which does not necessarily have to have a causal relationship with this treatment" (ICH E2A). In consultation with regulatory
authorities, sponsors may extend or limit the scope of adverse event collection (e.g., collecting pre-treatment events related to trial conduct, not collecting
events that are assessed as efficacy endpoints). The events included in the AE dataset should be consistent with the protocol requirements. Adverse events
may be captured either as free text or via a pre-specified list of terms.

Adverse Event Description and Coding
a. AETERM captures the verbatim term collected for the event. It is the topic variable for the AE dataset. AETERM is a required variable and must have
a value.
b. AEMODIFY is a permissible variable and should be included if the sponsor‘s procedure permits modification of a verbatim term for coding. The
modified term is listed in AEMODIFY. The variable should be populated as per the sponsor‘s procedures.
c. AEDECOD is the preferred term derived by the sponsor from the coding dictionary. It is a required variable and must have a value. It is expected that
the reported term (AETERM) will be coded using a standard dictionary such as MedDRA. The sponsor is expected to provide the dictionary name and
version used to map the terms utilizing the define.xml external codelist attributes.
d. AEBODSYS is the system organ class from the coding dictionary associated with the adverse event by the sponsor. This value may differ from the
primary system organ class designated in the coding dictionary's standard hierarchy. It is expected that this variable will be populated.
e. Sponsors may include the values of additional levels from the coding dictionary's hierarchy (i.e., High-Level Group Term, High-Level Term, LowerLevel Term) in the SUPPAE dataset as described in Appendix C5 (standard Supplemental Qualifier name codes) and Section 8.4.
193H

596H

Additional Categorization and Grouping
a. AECAT and AESCAT should not be redundant with the domain code or dictionary classification provided by AEDECOD and AEBODSYS (i.e., they
should provide a different means of defining or classifying AE records). AECAT and AESCAT are intended for categorizations that are defined in
advance. For example, a sponsor may have a separate CRF page for AEs of special interest and then another page for all other AEs. AECAT and
AESCAT should not be used for after-the-fact categorizations such as clinically significant. In cases where a category of AEs of special interest
resembles a part of the dictionary hierarchy (e.g., "CARDIAC EVENTS"), the categorization represented by AECAT and AESCAT may differ from the
categorization derived from the coding dictionary.
b. AEGRPID may be used to link (or associate) different records together to form a block of related records at the subject level within the AE domain.
See Section 4.1.2.6 for discussion of grouping variables.
597H

Pre-Specified Terms; Presence or Absence of Events
a. Adverse events are generally collected in two different ways, either by recording free text or using a pre-specified list of terms. In the latter case, the
solicitation of information on specific adverse events may affect the frequency at which they are reported; therefore, the fact that a specific adverse
event was solicited may be of interest to reviewers. An AEPRESP value of ―Y‖ is used to indicate that the event in AETERM was pre-specified on the
CRF.

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CDISC SDTM Implementation Guide (Version 3.1.2)
b.

If it is important to know which adverse events from a pre-specified list were not reported as well as those that did occur, these data should be
submitted in a Findings class dataset such as Findings About Events and Interventions (FA, Section 6.4). A record should be included in that Findings
dataset for each pre-specified adverse-event term. Records for adverse events that actually occurred should also exist in the AE dataset with AEPRESP
set to ―Y.‖
If a study collects both pre-specified adverse events as well as free-text events, the value of AEPRESP should be ―Y‖ for all pre-specified events and
null for events reported as free-text. AEPRESP is a permissible field and may be omitted from the dataset if all adverse events were collected as free
text.
598H

When adverse events are collected with the recording of free text, a record may be entered into the sponsor‘s data management system to indicate ―no
adverse events‖ for a specific subject. For these subjects, do not include a record in the AE submission dataset to indicate that there were no events.
Records should be included in the submission AE dataset only for adverse events that have actually occurred.

Timing Variables
a. Relative timing assessment ―Ongoing‖ is common in the collection of Adverse Event information. AEENRF may be used when this relative timing
assessment is made coincident with the end of the study reference period for the subject represented in the Demographics dataset (RFENDTC).
AEENRTPT with AEENTPT may be used when "Ongoing" is relative to another date such as the final safety follow-up visit date. See Section 4.1.4.7.
b. Additional timing variables (such as AEDTC) may be used when appropriate.
59H

Other Qualifier Variables
a. If categories of serious events are collected secondarily to a leading question, as in the example below, the values of the variables that capture reasons
an event is considered serious (i.e., AESCAN, AESCONG, etc.) may be null. For example, if Serious is answered ―No, ― the values for these variables
may be null. However, if Serious is answered ―Yes, ― at least one of them will have a ―Y‖ response. Others may be N or null, according to the
sponsor‘s convention.
Serious? [ ] Yes [ ] No
If yes, check all that apply: [ ] Fatal [ ] Life-threatening [ ] Inpatient hospitalization… [ ] etc.
On the other hand, if the CRF is structured so that a response is collected for each seriousness category, all category variables (e.g., AESDTH,
AESHOSP) would be populated and AESER would be derived.
b. The serious categories ―Involves cancer‖ (AESCAN) and ―Occurred with overdose‖ (AESOD) are not part of the ICH definition of a serious adverse
event, but these categories are available for use in studies conducted under guidelines that existed prior to the FDA‘s adoption of the ICH definition.
c. When a description of Other Medically Important Serious Adverse Events category is collected on a CRF, sponsors should place the description in the
SUPPAE dataset using the standard supplemental qualifier name code AESOSP as described in Section 8.4 and Appendix C5.
d. In studies using toxicity grade according to a standard toxicity scale such as Common Terminology Criteria for Adverse Events v3.0 (CTCAE),
published by the NCI (National Cancer Institute) at http://ctep.cancer.gov/reporting/ctc.html)), AETOXGR should be used instead of AESEV. In most
cases, either AESEV or AETOXGR is populated but not both. There may be cases when a sponsor may need to populate both variables. The sponsor is
expected to provide the dictionary name and version used to map the terms utilizing the define.xml external codelist attributes
60H

601H

AE Structure
The structure of the AE domain is one record per adverse event per subject. It is the sponsor's responsibility to define an event. This definition may
vary based on the sponsor's requirements for characterizing and reporting product safety and is usually described in the protocol. For example, the

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CDISC SDTM Implementation Guide (Version 3.1.2)
sponsor may submit one record that covers an adverse event from start to finish. Alternatively, if there is a need to evaluate AEs at a more granular
level, a sponsor may submit a new record when severity, causality, or seriousness changes or worsens. By submitting these individual records, the
sponsor indicates that each is considered to represent a different event. The submission dataset structure may differ from the structure at the time of
collection. For example, a sponsor might collect data at each visit in order to meet operational needs, but submit records that summarize the event and
contain the highest level of severity, causality, seriousness, etc. Examples of dataset structure:
1. One record per adverse event per subject for each unique event. Multiple adverse event records reported by the investigator are submitted
as summary records ―collapsed‖ to the highest level of severity, causality, seriousness, and the final outcome.
2. One record per adverse event per subject. Changes over time in severity, causality, or seriousness are submitted as separate events.
Alternatively, these changes may be submitted in a separate dataset based on the Findings About Events and Interventions model (see
Section 6.4).
3. Other approaches may also be reasonable as long as they meet the sponsor's safety evaluation requirements and each submitted record
represents a unique event. The domain-level metadata (See Section 3.2) should clarify the structure of the dataset.
602H

Use of EPOCH and TAETORD
When EPOCH is included in the Adverse Event domain, it should be the epoch of the start of the adverse event. In other words, it should be based on
AESTDTC, rather than AEENDTC. The computational method for EPOCH in the define.xml should describe any assumptions made to handle cases
where an adverse event starts on the same day that a subject starts an epoch, if AESTDTC and SESTDTC are not captured with enough precision to
determine the epoch of the onset of the adverse event unambiguously. Similarly, if TAETORD is included in the Adverse Events domain, it should be
the value for the start of the adverse event, and the computational method in the define.xml should describe any assumptions.

Additional Events Qualifiers
The following Qualifiers would not be used in AE: --OCCUR, --STAT, and--REASND. They are the only Qualifiers from the SDTM Events Class not
in the AE domain. They are not permitted because the AE domain contains only records for adverse events that actually occurred. See Assumption 4b
above for information on how to deal with negative responses or missing responses to probing questions for pre-specified adverse events.

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6.2.1.2 EXAMPLES FOR ADVERSE EVENTS DOMAIN MODEL
Example 1
This is an example of data from an AE CRF that collects AE terms as free text. The first study drug was administered to the subject on October 13, 2006 at 12:00.
Three AEs were reported. AEs were coded using MedDRA, and the sponsor‘s procedures include the possibility of modifying the reported term to aid in coding.
The CRF is structured so that seriousness category variables (e.g., AESDTH, AESHOSP) are checked only when AESER is answered ―Y.‖
Rows 1 and 2

Row 3

Show the following:
-an example of modifying the reported term for coding purposes. The modified value is in AEMODIFY.
-an example of the overall seriousness question AESER answered with an ―N‖ and corresponding seriousness category variables (e.g.,
AESDTH, AESHOSP) left blank.
Shows an example of the overall seriousness question AESER answered with a ―Y‖ and the relevant corresponding seriousness category
variables (AESHOSP and AESLIFE) answered with a ―Y‖. The other seriousness category variables are left blank. This row also shows an
example of AEENRF being populated because the AE was marked as ―Continuing‖ as of the end of the study reference period for the subject
(see Section 4.1.4.7).
603H

Row
1
2
3

STUDYID
ABC123
ABC123
ABC123

Row
1 (cont)
2 (cont)
3 (cont)

DOMAIN
AE
AE
AE

USUBJID AESEQ
AETERM
AESTDTC
123101
1
POUNDING HEADACHE 2005-10-12
123101
2
BACK PAIN FOR 6 HOURS 2005-10-13T13:05
123101
3
PULMONARY EMBOLISM 2005-10-21

AEBODSYS
Nervous system disorders
Musculoskeletal and connective tissue disorders
Vascular disorders

Row
AEOUT
1 (cont) RECOVERED/RESOLVED
2 (cont) RECOVERED/RESOLVED
3 (cont) RECOVERING/RESOLVING

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AESCONG

AESDISAB

AESEV
SEVERE
MODERATE
MODERATE
AESDTH

AEENDTC
2005-10-12
2005-10-13T19:00

AESER
N
N
Y

AESHOSP

AESLIFE

AEMODIFY
AEDECOD
HEADACHE Headache
BACK PAIN Back pain
Pulmonary embolism

AEACN
NOT APPLICABLE
DOSE REDUCED
DOSE REDUCED
AESMIE

AEREL
DEFINITELY NOT RELATED
PROBABLY RELATED
PROBABLY NOT RELATED

AESTDY
-1
1
9

AEENDY
-1
1

AEENRF

AFTER

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 2
In this example, a CRF module occurring at several visits asks whether or not nausea, vomiting, or diarrhea occurred. The responses to the probing questions
(Yes, No, or Not done) will be represented in the Findings About (FA) domain (see Section 6.4). If ―Yes ‖ the investigator is instructed to complete the Adverse
Event CRF. In the Adverse Events dataset, data on AEs solicited by means of pre-specified on the CRF will have an AEPRESP value of Y. For AEs solicited by a
general question, AEPRESP will be null. RELREC may be used to relate AE records with FA records.
604H

Rows 1 and 2 Show that nausea and vomiting were pre-specified on a CRF, as indicated by AEPRESP = ―Y‖. The subject did not experience diarrhea, so no
record for that term exists in the AE dataset.
Row 3

Shows an example of an AE (headache) that is not pre-specified on a CRF as indicated by a blank for AEPRESP

Row STUDYID DOMAIN
USUBJID
ABC123
AE
123101
1
ABC123
AE
123101
2
ABC123
AE
123101
3
Row
AEACN
1 (cont) DOSE REDUCED
2 (cont) DOSE REDUCED
3 (cont) DOSE NOT CHANGED

AESEQ
1
2
3

AEREL
RELATED
RELATED
POSSIBLY RELATED

AETERM
NAUSEA
VOMITING
HEADACHE

AEDECOD
Nausea
Vomiting
Headache

AEOUT
RECOVERED/RESOLVED
RECOVERED/RESOLVED
RECOVERED/RESOLVED

AEPRESP
AEBODSYS
AESEV
Y
Gastrointestinal disorders SEVERE
Y
Gastrointestinal disorders MODERATE
Nervous system disorders MILD

AESTDTC
2005-10-12
2005-10-13T13:00
2005-10-21

AEENDTC
2005-10-13
2005-10-13T19:00
2005-10-21

AESER
N
N
N
AESTDY
2
3
11

AEENDY
3
3
11

Example 3
In this example, a CRF module occurs only once and asks whether or not nausea, vomiting, or diarrhea occurred. In the context of this study, the conditions that
occurred are reportable as Adverse Events. No additional data about these events is collected. No other adverse event information is collected via general
questions. The responses to the probing questions (Yes, No, or Not done) will be represented in the Findings About (FA) domain (see Section 6.4). Since all
adverse events must be submitted in AE dataset, this represents an unusual case; the AE dataset will be populated with the term and the flag indicating that it was
pre-specified, but timing information is limited to the date of collection, and other expected Qualifiers are not available. RELREC may be used to relate AE
records with FA records.
605H

Rows 1 and 2 Subject was found to have experienced nausea and vomiting by means of the probing questions. The subject did not experience diarrhea, so no
record for that term exists in the AE dataset
Row STUDYID

DOMAIN

USUBJID

AESEQ

AETERM

AEDECOD

AEPRESP

ABC123

123101

NAUSEA

Nausea

ABC123

123101

VOMITING

Vomiting

AEBODSYS
Gastrointestinal
disorders
Gastrointestinal
disorders

AESER

AEACN

AEREL AESTDTC AEENDTC

AEDTC

AEDY

2005-10-29 19
2005-10-29 19

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CDISC SDTM Implementation Guide (Version 3.1.2)
Example 4
In this example, the investigator was instructed to create a new adverse-event record each time the severity of an adverse event changes. AEGRPID can be used
to identify the group of records related to a single event for a subject.
Row 1
Rows 2-6
Row
1
2
3
4
5
6

Shows an adverse event of nausea, whose severity was moderate.
Show how AEGRPID can be used to identify the group of records related to a single event for a subject.

STUDYID
ABC123
ABC123
ABC123
ABC123
ABC123
ABC123

Row
1 (cont‘d)
2 (cont‘d)
3 (cont‘d)
4 (cont‘d)
5 (cont‘d)
6 (cont‘d)

AESER
N
N
N
N
N
N

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DOMAIN
AE
AE
AE
AE
AE
AE

USUBJID
123101
123101
123101
123101
123101
123101

AEACN
DOSE NOT CHANGED
DOSE NOT CHANGED
DOSE NOT CHANGED
DOSE NOT CHANGED
DOSE NOT CHANGED
DOSE NOT CHANGED

AESEQ
1
2
3
4
5
6

AEGRPID
1
1
1
2
2

AEREL
RELATED
POSSIBLY RELATED
POSSIBLY RELATED
POSSIBLY RELATED
POSSIBLY RELATED
POSSIBLY RELATED

AETERM
NAUSEA
VOMITING
VOMITING
VOMITING
DIARRHEA
DIARRHEA
AESTDTC
2005-10-13
2005-10-14
2005-10-16
2005-10-17
2005-10-16
2005-10-17

AEBODSYS
Gastrointestinal disorders
Gastrointestinal disorders
Gastrointestinal disorders
Gastrointestinal disorders
Gastrointestinal disorders
Gastrointestinal disorders

AESEV
MODERATE
MILD
SEVERE
MILD
SEVERE
MODERATE

AEENDTC
2005-10-14
2005-10-16
2005-10-17
2005-10-20
2005-10-17
2005-10-21

CDISC SDTM Implementation Guide (Version 3.1.2)

6.2.2 DISPOSITION — DS
ds.xpt, Disposition — Events, Version 3.1.2. One record per disposition status or protocol milestone per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char DS
Identifier Two-character abbreviation for the domain.
196H

Core

References

Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4
SDTMIG 4.1.2.3
SDTM 2.2.4
60H

607H

USUBJID

Unique Subject Identifier

Char

DSSEQ

Sequence Number

Num

DSGRPID

Group ID

Char

DSREFID
DSSPID

Reference ID
Char
Sponsor-Defined Identifier Char

DSTERM

Reported Term for the
Disposition Event

Char

Standardized Disposition
Term

Char (NCOMPLT)

Category for Disposition
Event

Char (DSCAT)

Char *

EPOCH

Subcategory for
Disposition Event
Epoch

DSDTC

Date/Time of Collection

Char ISO 8601

Identifier Identifier used to uniquely identify a subject across all studies for all
Req
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a Req
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain for a
Perm
subject.
Identifier Internal or external identifier.
Perm
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm
explicit line identifier or defined in the sponsor‘s operational database.
Example: Line number on a Disposition page.
Topic
Verbatim name of the event or protocol milestone. Some terms in
Req
DSTERM will match DSDECOD, but others, such as ―Subject moved‖
will map to controlled terminology in DSDECOD, such as ―LOST TO
FOLLOW-UP.‖
Synonym Controlled terminology for the name of disposition event or protocol
Req
Qualifier milestone. Examples of protocol milestones: INFORMED CONSENT
OBTAINED, RANDOMIZED
Grouping Used to define a category of related records. DSCAT is now an
Exp
Qualifier ―Expected‖ variable. DSCAT was permissible in SDTMIG 3.1.1 and
earlier versions. The change from ―permissible‖ to ―expected‖ is based on
the requirement to distinguish protocol milestones and/or other events
from disposition events. DSCAT may be null if there are only ―disposition
events‖; however, it is recommended that DSCAT always be populated.
Grouping A further categorization of disposition event.
Perm
Qualifier
Timing
EPOCH may be used when DSCAT = ―DISPOSITION EVENT‖.
Perm
Examples: SCREENING, TREATMENT PHASE, FOLLOW-UP
Timing
Perm

DSSTDTC

Start Date/Time of
Disposition Event

Char ISO 8601

Timing

DSDECOD

DSCAT

DSSCAT

197H

615H

Char *

608H9

SDTMIG 4.1.2.6
SDTM 2.2.4
SDTM 2.2.4
SDTM 2.2.4
610H

SDTM 2.2.2,
SDTMIG 4.1.3.6
61H

SDTM 2.2.2,
SDTMIG 4.1.3.5
612H34

SDTM 2.2.2,
SDTMIG 4.1.2.6
61H

SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.5,
SDTMIG 7.1.2
SDTM 2.2.5,
SDTMIG 4.1.4.1
SDTM 2.2.5,
SDTMIG 4.1.4.1
617H

618H

619H

Exp
620H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
DSSTDY

Variable Label
Study Day of Start of
Disposition Event

Controlled
Type Terms, Codelist
Role
or Format
Num
Timing

CDISC Notes

Core

Study day of start of event relative to the sponsor-defined RFSTDTC.

Perm

References
SDTM 2.2.5,
SDTMIG 4.1.4.4
621H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.2.2.1 ASSUMPTIONS FOR DISPOSITION DOMAIN MODEL
1. DS Definition
The Disposition dataset provides an accounting for all subjects who entered the study and may include protocol milestones, such as randomization, as well as
the subject's completion status or reason for discontinuation for the entire study or each phase or segment of the study, including screening and post-treatment
follow-up. Sponsors may choose which disposition events and milestones to submit for a study. See ICH E3, Section 10.1 for information about disposition
events.
2. Categorization
a. DSCAT is used to distinguish between disposition events, protocol milestones and other events. The controlled terminology for DSCAT consists of
―DISPOSITION EVENT,‖ ―PROTOCOL MILESTONE,‖ and ―OTHER EVENT.‖
b.

A ―DISPOSITION EVENT‖ describes whether a subject completed the study or portion of a study (Epoch) or the reason they did not complete. The
subject‘s disposition is often described for each study Epoch (e.g., screening, initial treatment, washout, cross-over treatment, follow-up).

A ―PROTOCOL MILESTONE‖ is a protocol-specified, ―point-in-time‖ event. The most common protocol milestones are ―INFORMED CONSENT
OBTAINED‖ and ―RANDOMIZED.‖

Other important events that occur during a trial, but are not driven by protocol requirements and are not captured in another Events or Interventions class
dataset, are classified as ―OTHER EVENT.‖ ―TREATMENT UNBLINDED‖ is an example of ―OTHER EVENT.‖

3. DS Description and Coding
a. DSTERM and DSDECOD are required. DSDECOD values are drawn from sponsor-defined controlled terminology. The controlled terminology will
depend on the value of DSCAT. When DSCAT="DISPOSITION EVENT", DSTERM contains either "COMPLETE" or, if the subject did not complete,
specific verbatim information about the disposition event.
b.

When DSTERM = "COMPLETED", DSDECOD = "COMPLETED". When DSTERM contains verbatim text, DSDECOD will contain a standard term
from a controlled terminology list. For example, DSTERM = "Subject moved" might map to "LOST TO FOLLOW-UP" in the sponsor's controlled
terminology.

A sponsor may collect one disposition event for the trial as a whole, or they may collect disposition for each Epoch of the trial. When disposition is
collected for each Epoch, the variable EPOCH should be included in the DS dataset. When EPOCH is populated for disposition events (records with
DSCAT = DISPOSITION EVENT), EPOCH is the name of the Epoch for the disposition event described in the record. This is a subtly different
meaning from that of EPOCH when it is used in other general-observation-class domains, where EPOCH, as a Timing variable, is the name of the Epoch
during which --STDTC or --DTC falls. The values of EPOCH are drawn from the Trial Arms domain, Section 7.2.
62H

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CDISC SDTM Implementation Guide (Version 3.1.2)
d.

When DSCAT="PROTOCOL MILESTONE", DSTERM and DSDECOD will contain the same value drawn from the sponsor's controlled terminology.
Examples of controlled terms include "INFORMED CONSENT OBTAINED" and "RANDOMIZED." EPOCH should not be populated when DSCAT =
―PROTOCOL MILESTONE‖.

Events that are not disposition or milestone related are classified as an ―OTHER EVENT‖ (see Assumption 2d above). If a reason for the OTHER
EVENT was collected, then the reason is in DSTERM. For example, treatment was unblinded due to investigator error. DSTERM = INVESTIGATOR
ERROR and DSDECOD = TREATMENT UNBLINDED. IF no reason was collected then DSTERM = DSDECOD.

4. Timing Variables
a. DSSTDTC is expected and is used for the date/time of the disposition event. Disposition events do not have start and end dates since disposition events
do not span an interval (e.g. randomization date) but occur at a single date/time (e.g., randomization date).
b. DSSTDTC documents the date/time that a protocol milestone, disposition event, or other event occurred. In the case of a disposition event, the reason
for not completing the referenced study Epoch may be related to an event or intervention reported in another dataset. DSSTDTC is the date/time that the
Epoch was completed and is not necessarily the same as the start or end date/time of the event or intervention that led to discontinuation. For example, a
subject reported severe vertigo on June 1, 2006 (AESTDTC). After ruling out other possible causes, the investigator decided to discontinue study
treatment on June 6, 2006 (DSSTDTC). The subject reported that the vertigo had resolved on June 8, 2006 (AEENDTC).
5. Reasons for Termination
a. ICH E3, Section 10.1 indicates that ―the specific reason for discontinuation‖ should be presented, and that summaries should be ―grouped by treatment
and by major reason.‖ The CDISC SDS Team interprets this guidance as requiring one standardized disposition term (DSDECOD) per disposition event.
If multiple reasons are reported, the sponsor should identify a primary reason and use that to populate DSTERM and DSDECOD. Additional reasons
should be submitted in SUPPDS. Example:
DSTERM= SEVERE NAUSEA
DSDECOD=ADVERSE EVENT
SUPPDS QNAM=DSTERM1
SUPPDS QLABEL= Reported Term for Disposition Event 1
SUPPDS QVAL=SUBJECT REFUSED FURTHER TREATMENT
SUPPDS QNAM=DSDECOD1
SUPPDS QLABEL= Standardized Disposition Term 1
SUPPDS QVAL=WITHDREW CONSENT
6. Additional Event Qualifiers
The following Qualifiers would generally not be used in DS: --PRESP, --OCCUR, --STAT, --REASND, --BODSYS, --LOC, --SEV, --SER, --ACN,
--ACNOTH, --REL, --RELNST, --PATT, --OUT, --SCAN, --SCONG, --SDISAB, --SDTH, --SHOSP, --SLIFE, --SOD, --SMIE, --CONTRT, --TOXGR.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.2.2.2 EXAMPLES FOR DISPOSITION DOMAIN MODEL
Example 1
In this example, a DS CRF collected multiple disposition events at different time points in the study indicated by EPOCH. There are also several protocol
milestones which are indicated by DSCAT = ―PROTOCOL MILESTONE‖. DSTERM is populated with controlled terminology with the same value as
DSDECOD except in the case when there is free text for DSTERM such as ―Subject moved‖. In this case, the controlled terminology is only in DSDECOD
(LOST TO FOLLOW-UP).
Rows 1-21: There are multiple disposition events and protocol milestones per subject. EPOCH is populated when DSCAT has a value of DISPOSITION
EVENT and null when DSCAT has value of PROTOCOL MILESTONE.
Rows 2, 4, 5: Subject 123101 has 3 records to indicate the completion of 3 stages of the study, which are screening, treatment phase, and follow-up. The study
also collected the protocol milestones of INFORMED CONSENT and RANDOMIZATION.
Row 7:
Subject 123102 is a screen drop (also known as a screen failure). Screen drops are identified by a DSDECOD that is not equal to ―COMPLETED‖
for the SCREENING stage. This is an example of the submission of the verbatim reason for discontinuation in DSTERM. Also note that although
DSDECOD is ―PROTOCOL VIOLATION‖, this record represents the disposition event for the SCREENING stage and documents the reason for
not completing (―SUBJECT DENIED MRI PROCEDURE‖) and the corresponding date of discontinuation (DSSTDTC). A record describing the
protocol deviation event itself should appear in the DV dataset.
Rows 9, 11: Subject 123103 completed the screening stage but did not complete the treatment stage.
Row 11:
The verbatim reason the subject dropped is in DSTERM (SUBJECT MOVED) and the controlled term is in DSDECOD (LOST TO FOLLOW-UP).
Row 16:
Subject 123104 died in an automobile accident on October 29, 2003 (see DSSTDTC) after the completion of treatment, but prior to the completion
of follow-up. Note that the date of collection of the event information (DSDTC = October 31, 2003) was different from the date of the disposition
event.
Rows 20, 21: Subject 123105 discontinued study treatment due to an AE, but went on to complete the follow-up phase of the trial.

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CDISC SDTM Implementation Guide (Version 3.1.2)
Row STUDYID DOMAIN

USUBJID

DSSEQ

ABC123

123101

INFORMED CONSENT INFORMED CONSENT
OBTAINED
OBTAINED

PROTOCOL MILESTONE

ABC123

123101

COMPLETED

DISPOSITION EVENT

ABC123

123101

RANDOMIZED

PROTOCOL MILESTONE

ABC123

123101

COMPLETED

DISPOSITION EVENT

ABC123

123101

COMPLETED

DISPOSITION EVENT

ABC123

123102

INFORMED CONSENT INFORMED CONSENT
OBTAINED
OBTAINED

PROTOCOL MILESTONE

ABC123

123102

SUBJECT DENIED MRI PROTOCOL VIOLATION
PROCEDURE

DISPOSITION EVENT

ABC123

123103

INFORMED CONSENT INFORMED CONSENT
OBTAINED
OBTAINED

PROTOCOL MILESTONE

4
5
6
7

DSTERM

DSDECOD

DSCAT

EPOCH

DSDTC

DSSTDTC

2003-09-21

2003-09-29

2003-09-30

TREATMENT
PHASE

2003-10-31

FOLLOW-UP

2003-11-15

2003-11-21

2003-11-22

2003-11-20

2003-09-15

2003-09-22

2003-09-30

2003-10-31

2003-09-15

2003-09-22

2003-09-30

SCREENING

ABC123

123103

COMPLETED

DISPOSITION EVENT

ABC123

123103

RANDOMIZED

PROTOCOL MILESTONE

ABC123

123103

SUBJECT MOVED

LOST TO FOLLOW-UP

DISPOSITION EVENT

ABC123

123104

INFORMED CONSENT INFORMED CONSENT
OBTAINED
OBTAINED

PROTOCOL MILESTONE

ABC123

123104

COMPLETED

DISPOSITION EVENT

ABC123

123104

RANDOMIZED

PROTOCOL MILESTONE

ABC123

123104

COMPLETED

DISPOSITION EVENT

TREATMENT
PHASE

2003-10-15

ABC123

123104

AUTOMOBILE
ACCIDENT

DEATH

DISPOSITION EVENT

FOLLOW-UP

2003-10-31

2003-10-29

ABC123

123105

INFORMED CONSENT INFORMED CONSENT
OBTAINED
OBTAINED

PROTOCOL MILESTONE

2003-09-28

ABC123

123105

COMPLETED

DISPOSITION EVENT

2003-10-02

ABC123

123105

RANDOMIZED

PROTOCOL MILESTONE

2003-10-02

ABC123

123105

ANEMIA

ADVERSE EVENT

DISPOSITION EVENT

TREATMENT
PHASE

2003-10-17

ABC123

123105

COMPLETED

DISPOSITION EVENT

FOLLOW-UP

2003-11-02

20
21

SCREENING
TREATMENT
PHASE

SCREENING

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CDISC SDTM Implementation Guide (Version 3.1.2)
Example 2
In this example, the sponsor has chosen to simply submit whether or not the subject completed the study, so there is only one record per subject.
Row 1:
Subject who completed the study
Rows 2, 3:
Subjects who discontinued.
Row

STUDYID

DOMAIN

USUBJID

DSSEQ

DSTERM

DSDECOD

DSCAT

DSSTDTC

ABC456

456101

COMPLETED

DISPOSITION EVENT

2003-09-21

ABC456

456102

SUBJECT TAKING STUDY MED
ERRATICALLY

PROTOCOL VIOLATION

DISPOSITION EVENT

2003-09-29

ABC456

456103

LOST TO FOLLOW-UP

DISPOSITION EVENT

2003-10-15

Example 3
Rows 1, 2:
Rows 3, 5:
Row 4:

Row

Subject completed the treatment and follow-up phase
Subject did not complete the treatment phase but did complete the follow-up phase.
Subject‘s treatment is unblinded. The date of the unblinding is represented in DSSTDTC. Maintaining the blind as per protocol is not considered
to be an event since there is no change in the subject‘s state.
DOMAIN

USUBJID

DSSEQ

DSTERM

DSDECOD

ABC789

789101

COMPLETED

DISPOSITION EVENT

TREATMENT PHASE

2004-09-12

ABC789

789101

COMPLETED

DISPOSITION EVENT

FOLLOW-UP

2004-12-20

ABC789

789102

SKIN RASH

ADVERSE
EVENT

DISPOSITION EVENT

TREATMENT PHASE

2004-09-30

ABC789

789102

SUBJECT HAD
SEVERE RASH

TREATMENT
UNBLINDED

OTHER EVENT

TREATMENT PHASE

2004-10-01

ABC789

789102

COMPLETED

DISPOSITION EVENT

FOLLOW-UP

2004-12-28

3
4

STUDYID

DSCAT

EPOCH

DSSTDTC

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CDISC SDTM Implementation Guide (Version 3.1.2)
Example 4
In this example, the CRF documents a link between the DS record and the AE record. This relationship is documented in the RELREC dataset.
Disposition (DS) Dataset
Row 1:
Shows that Subject died of heart failure.
Row

STUDYID

DOMAIN

USUBJID

DSSEQ

DSTERM

DSDECOD

ABC123

123102

Heart Failure

DEATH

DSCAT
DISPOSITION
EVENT

EPOCH
TREATMENT
PHASE

DSDTC

DSSTDTC

2003-09-29

Adverse Event (AE) Dataset:
Row 1:
Shows that Subject died due to heart failure.
Row STUDYID DOMAIN
1

ABC123

USUBJID

AESEQ

AETERM

AESTDTC

AEENDTC

123102

Heart Failure

2003-09-29

Row

AEREL

AEOUT

AESCAN

1 (cont)

DEFINITELY NOT RELATED

FATAL

AESCONG AESDISAB
N

AEDECOD
HEART
FAILURE

AEBODSYS
CARDIOVASCULAR
SYSTEM

AESEV

AESER

AEACN

SEVERE

NOT APPLICABLE

AESDTH

AESHOSP

AESLIFE

AESOD

AESMIE

RELREC Dataset
Rows 1, 2: Show that the subject‘s disposition status that is related to the AE record.
Row
1
2

STUDYID
ABC123
ABC123

RDOMAIN
DS
AE

USUBJID
123102
123102

IDVAR
DSSEQ
AESEQ

IDVARVAL
1
1

RELTYPE

RELID
1
1

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.2.3 MEDICAL HISTORY — MH
mh.xpt, Medical History — Events, Version 3.1.2. One record per medical history event per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char MH
Identifier Two-character abbreviation for the domain.
198H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4
SDTMIG 4.1.2.3
SDTM 2.2.4
623H

624H

USUBJID

Unique Subject Identifier

Char

MHSEQ

Sequence Number

Num

MHGRPID

Group ID

Char

MHREFID
MHSPID

Reference ID
Char
Sponsor-Defined Identifier Char

MHTERM

Reported Term for the
Medical History
MHMODIFY Modified Reported Term

Char

MHDECOD Dictionary-Derived Term

Char *

Req

Synonym
Qualifier
Synonym
Qualifier

Perm

625H

Req
Perm

SDTMIG 4.1.2.6,
SDTM 2.2.4
SDTM 2.2.4
SDTM 2.2.4

Perm
Perm

627H

Req

SDTM 2.2.2,
SDTMIG 4.1.3.6
SDTM 2.2.2,
SDTMIG 4.1.3.5
SDTM 2.2.2,
SDTMIG 4.1.3.5
628H

Char

If MHTERM is modified to facilitate coding, then MHMODIFY will
contain the modified text.
Dictionary-derived text description of MHTERM or MHMODIFY.
Equivalent to the Preferred Term (PT in MedDRA). The sponsor is

629H301

Perm
632H4

expected to provide the dictionary name and version used to map
the terms utilizing the define.xml external codelist attributes
MHCAT
MHSCAT
MHPRESP

Category for Medical
Char *
History
Subcategory for Medical
Char *
History
Medical History Event Pre- Char (NY)
Specified
19H

MHOCCUR Medical History
Occurrence

Char (NY)

MHSTAT

Char (ND)

Completion Status

1920H

192H

Grouping
Qualifier
Grouping
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier

Used to define a category of related records. Examples: CARDIAC or
GENERAL
A further categorization of the condition or event.

Perm

SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.7
SDTMIG 4.1.5.7
SDTM 2.2.2,
SDTMIG 4.1.5.7
635H

Perm
63H

A value of ―Y‖ indicates that this medical history event was pre-specified Perm
on the CRF. Values are null for spontaneously reported events (i.e., those
collected as free-text verbatim terms)
Used when the occurrence of specific medical history conditions is
Perm
solicited to indicate whether or not (Y/N) a medical condition (MHTERM)
had ever occurred. Values are null for spontaneously reported events.
The status indicates that the pre-specified question was not answered.
Perm
637H8

639H

640H

SDTM 2.2.2,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
641H

642H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name

Variable Label

MHREASND Reason Medical History
Not Collected

Controlled
Type Terms, Codelist
Role
or Format
Char
Record
Qualifier

CDISC Notes

Core

Describes the reason data for a pre-specified condition was not collected. Perm
Used in conjunction with MHSTAT when value is NOT DONE.

References
SDTM 2.2.2
SDTMIG 4.1.5.1
SDTMIG 4.1.5.7
SDTM 2.2.2,
SDTMIG 4.1.3.5
643H

64H

MHBODSYS Body System or Organ
Class

Char *

MHDTC

Char ISO 8601

Timing

Perm

Char ISO 8601

Timing

Perm

Char ISO 8601

Timing

Perm

Num

Timing

MHSTDTC
MHENDTC
MHDY

MHENRF

Date/Time of History
Collection
Start Date/Time of Medical
History Event
End Date/Time of Medical
History Event
Study Day of History
Collection

Record
Qualifier

Dictionary-derived. Body system or organ class that is involved in an
event or measurement from a standard hierarchy (e.g., MedDRA). When
using a multi-axial dictionary such as MedDRA, this should contain the
SOC used for the sponsor‘s analyses and summary tables which may not
necessarily be the primary SOC.

Perm
645H7

SDTM 2.2.5
SDTMIG 4.1.4.1
SDTM 2.2.5
SDTMIG 4.1.4.1
SDTM 2.2.5
SDTMIG 4.1.4.1
SDTM 2.2.5
SDTMIG 4.1.4.4
648H

649H

650H

End Relative to Reference Char (STENRF)
Period

Timing

MHENRTPT End Relative to Reference Char BEFORE,
Timing
Time Point
AFTER,
COINCIDENT,
ONGOING, U
MHENTPT End Reference Time Point Char
Timing

1. Study day of medical history collection, measured as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.
Describes the end of the event relative to the sponsor-defined reference
period. The sponsor-defined reference period is a continuous period of
time defined by a discrete starting point and a discrete ending point
(represented by RFSTDTC and RFENDTC in Demographics)
Identifies the end of the event as being before or after the reference time
point defined by variable MHENTPT.

Perm
651H

Perm

SDTM 2.2.5
SDTMIG 4.1.4.7
652H

Perm

Description or date/time in ISO 8601 character format of the reference
Perm
point referred to by MHENRTPT. Examples: "2003-12-25" or "VISIT 2".

SDTM 2.2.5
SDTMIG 4.1.4.7
653H

SDTM 2.2.5
SDTMIG 4.1.4.7
654H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.2.3.1 ASSUMPTIONS FOR MEDICAL HISTORY DOMAIN MODEL
1.

MH Definition
a. The Medical History dataset generally includes the subject's prior and concomitant conditions at the start of the trial. Examples of subject medical
history information could include general medical history and gynecological history. Note that prior and concomitant medications should be
submitted in an appropriate dataset from the Interventions class (e.g., CM).

Medical History Description and Coding
a. MHTERM captures the verbatim term collected for the condition or event. It is the topic variable for the MH dataset. MHTERM is a required
variable and must have a value.
b. MHMODIFY is a permissible variable and should be included if the sponsor‘s procedure permits modification of a verbatim term for coding. The
modified term is listed in MHMODIFY. The variable should be populated as per the sponsor‘s procedures; null values are permitted.
c. If the sponsor codes the reported term (MHTERM) using a standard dictionary, then MHDECOD will be populated with the preferred term
derived from the dictionary. The sponsor is expected to provide the dictionary name and version used to map the terms utilizing the define.xml
external codelist attributes
d. MHBODSYS is the system organ class from the coding dictionary associated with the adverse event by the sponsor. This value may differ from
the primary system organ class designated in the coding dictionary's standard hierarchy.
e. Sponsors may include the values of additional levels from the coding dictionary's hierarchy (e.g., High Level Group Term, High Level Term,
Lower Level Term) in the SUPPMH dataset as described in Section 8.4. See Appendix C5 for standard Supplemental Qualifier name codes.
f. If a CRF collects medical history by pre-specified body systems and the sponsor also codes reported terms using a standard dictionary, then
MHDECOD and MHBODSYS are populated using the standard dictionary. MHCAT and MHSCAT should be used for the pre-specified body
systems.
65H

192H

Additional Categorization and Grouping
a. MHCAT and MHSCAT may be populated with the sponsor's pre-defined categorization of medical history events, which are often pre-specified on
the CRF. Note that even if the sponsor uses the body system terminology from the standard dictionary, MHBODSYS and MHCAT may differ,
since MHBODSYS is derived from the coding system, while MHCAT is effectively assigned when the investigator records a condition under the
pre-specified category.
i. This categorization should not group all records (within the MH Domain) into one generic group such as ―Medical History‖ or ―General
Medical History‖ because this is redundant information with the domain code. If no smaller categorization can be applied, then it is not
necessary to include or populate this variable.
ii. Examples of MHCAT could include ―General Medical History― (see above assumption since if ―General Medical History‖ is an MHCAT
value then there should be other MHCAT values), ―Allergy Medical History, ― and ―Reproductive Medical History.‖
b. MHGRPID may be used to link (or associate) different records together to form a block of related records at the subject level within the MH
domain. It should not be used in place of MHCAT or MHSCAT, which are used to group data across subjects. For example, if a group of
syndromes reported for a subject were related to a particular disease then the MHGRPID variable could be populated with the appropriate text.

Pre-Specified Terms; Presence or Absence of Events
a. Information on medical history is generally collected in two different ways, either by recording free text or using a pre-specified list of terms. The
solicitation of information on specific medical history events may affect the frequency at which they are reported; therefore, the fact that a specific
medical history event was solicited may be of interest to reviewers. MHPRESP and MHOCCUR are used together to indicate whether the condition
in MHTERM was pre-specified and whether it occurred, respectively. A value of ―Y‖ in MHPRESP indicates that the term was pre-specified.

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CDISC SDTM Implementation Guide (Version 3.1.2)
b.
c.
d.

MHOCCUR is used to indicate whether a pre-specified medical condition occurred; a value of Y indicates that the event occurred and N indicates
that it did not.
If a medical history event was reported using free text, the values of MHPRESP and MHOCCUR should be null. MHPRESP and MHOCCUR are
permissible fields and may be omitted from the dataset if all medical history events were collected as free text.
MHSTAT and MHREASND provide information about pre-specified medical history questions for which no response was collected. MHSTAT and
MHREASND are permissible fields and may be omitted from the dataset if all medications were collected as free text or if all pre-specified
conditions had responses in MHOCCUR.
Situation
Spontaneously reported event occurred
Pre-specified event occurred
Pre-specified event did not occur
Pre-specified event has no response

Value of MHPRESP

Value of MHOCCUR

Y
Y
Y

Y
N

Value of MHSTAT

NOT DONE

When medical history events are collected with the recording of free text, a record may be entered into the data management system to indicate ―no
medical history‖ for a specific subject or pre-specified body system category (e.g., Gastrointestinal). For these subjects or categories within subject,
do not include a record in the MH dataset to indicate that there were no events.

Timing Variables
a. Relative timing assessments such as ―Ongoing‖ or "Active" are common in the collection of Medical History information. MHENRF may be used
when this relative timing assessment is coincident with the start of the study reference period for the subject represented in the Demographics
dataset (RFSTDTC). MHENRTPT and MHENTPT may be used when "Ongoing" is relative to another date such as the screening visit date. See
examples below and Section 4.1.4.7.
b. Additional timing variables (such as MHSTRF) may be used when appropriate.
65H

Additional Events Qualifiers
The following Qualifiers would generally not be used in MH: --SER, --ACN, --ACNOTH, --REL, --RELNST, --OUT, --SCAN, --SCONG, --SDISAB,
--SDTH, --SHOSP, --SLIFE, --SOD, --SMIE.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.2.3.2 EXAMPLES FOR MEDICAL HISTORY DOMAIN MODEL
Example 1
In this example, a General Medical History CRF collects verbatim descriptions of conditions and events by body system (e.g., Endocrine, Metabolic) and asks
whether or not the conditions were ongoing at the time of the visit. Another CRF page is used for Cardiac history events and for primary diagnosis; this page does
not include the ongoing question.
Rows 1-3:
MHSCAT displays the body systems specified on the General Medical History CRF. The reported events are coded using a standard dictionary.
MHDECOD and MHBODSYS display the preferred term and body system assigned through the coding process.
Rows 1-3:
MHENRTPT has been populated based on the response to the "Ongoing " question on the General Medical History CRF. MHENTPT displays the
reference date for MHENRTPT - that is, the date the information was collected. If "Yes" is specified for Ongoing, MHENRTPT="ONGOING" if
"No" is checked, MHENRTPT="BEFORE." See Section 4.1.4.7 for further guidance.
Row 4:
MHCAT indicates that this record displays the primary diagnosis, "ISCHEMIC STROKE". This term was not coded.
Row 5:
MHCAT indicates that this term was reported on the Cardiac Medical History page.
657H

Row STUDYID DOMAIN USUBJID MHSEQ

MHTERM

MHDECOD

MHCAT

MHSCAT

MHBODSYS

MHSTDTC

MHENRTPT

MHENTPT

ABC123

123101

ASTHMA

Asthma

GENERAL
MEDICAL
HISTORY

RESPIRATORY Respiratory system
disorders

ONGOING

2004-09-18

ABC123

123101

FREQUENT
HEADACHES

Headache

GENERAL
MEDICAL
HISTORY

CNS

Central and peripheral
nervous system
disorders

ONGOING

2004-09-18

ABC123

123101

BROKEN LEG Bone fracture GENERAL
MEDICAL
HISTORY

OTHER

Musculoskeletal
system disorders

BEFORE

2004-09-18

ABC123

123101

ISCHEMIC
STROKE

ABC123

123101

CHF

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November 12, 2008

PRIMARY
DIAGNOSIS
Cardiac
failure
congestive

CARDIAC
MEDICAL
HISTORY

2004-09-17T07:30
Cardiac disorders

2004-06

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 2
This is an example of a medical history CRF where the history of specific (prespecified) conditions is solicited. The conditions are not coded using a standard
dictionary. The data are collected as part of the Screening visit.
Rows 1-10
MHPRESP values of ―Y‖ indicate that each condition was pre-specified on the CRF. The presence or absence of a condition is documented
with MHOCCUR. The data are collected as part of the Screening visit.
Rows 1-3, 7, 9: The absence of a condition is documented with MHOCCUR.
Rows 4-6, 8:
The presence of a condition is documented with MHOCCUR.
Row 10:
The question regarding ASTHMA was not asked. MHSTAT is used to indicate this and MHOCCUR is null.
Row STUDYID

DOMAIN

USUBJID MHSEQ

MHTERM

MHPRESP MHOCCUR MHSTAT MHREASND

VISIT

VISITNUM MHDTC MHDY

ABC123

101002

HISTORY OF EARLY
CORONARY ARTERY DISEASE
(<55 YEARS OF AGE)

SCREEN

2006-04-22

-5

ABC123

101002

CONGESTIVE HEART FAILURE

SCREEN

2006-04-22

-5

ABC123

101002

PERIPHERAL VASCULAR
DISEASE

SCREEN

2006-04-22

-5

ABC123

101002

TRANSIENT ISCHEMIC ATTACK

SCREEN

2006-04-22

-5

ABC123

101002

ASTHMA

SCREEN

2006-04-22

-5

ABC123

101003

HISTORY OF EARLY
CORONARY ARTERY DISEASE
(<55 YEARS OF AGE)

SCREEN

2006-05-03

-3

ABC123

101003

CONGESTIVE HEART FAILURE

SCREEN

2006-05-03

-3

ABC123

101003

PERIPHERAL VASCULAR
DISEASE

SCREEN

2006-05-03

-3

ABC123

101003

TRANSIENT ISCHEMIC ATTACK

SCREEN

2006-05-03

-3

10 ABC123

101003

ASTHMA

FORGOT TO SCREEN
ASK

2006-05-03

-3

NOT
DONE

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CDISC SDTM Implementation Guide (Version 3.1.2)
Example 3
In this example, three CRFs related to medical history are collected:
 A General Medical History CRF collects descriptions of conditions and events by body system (e.g., Endocrine, Metabolic) and asks whether or not the
conditions were ongoing at study start. The reported events are coded using a standard dictionary.
 A second CRF collects Stroke History.
 A third CRF asks whether or not the subject had any of a list of 4 specific risk factors, with space for the investigator to write in other risk factors.
MHCAT is used to indicate the CRF from which the medical condition came.
Rows 1-3:
Rows 1-3:

MHSCAT displays the body systems specified on the General Medical History CRF. The reported events are coded using a standard dictionary.
MHENRF has been populated based on the response to the "Ongoing at Study Start" question on the General Medical History CRF. If "Yes" is specified,
MHENRF="DURING/AFTER;" if "No" is checked, MHENRF="BEFORE" See Section 4.1.4.7 for further guidance on using --STRF and --ENRF.
MHCAT indicates that this record displays Stroke History. This term is not coded.
MHPRESP and MHOCCUR are null for the conditions, which are not prespecified .
MHCAT indicates that these terms were reported on the RISK FACTORS page. These terms are not coded.
MHPRESP values of ―Y‖ indicate that each risk factor was pre-specified on the CRF. MHOCCUR is populated with Y or N corresponding to the
CRF response to the questions for the 4 pre-specified risk factors.
MHPRESP and MHOCCUR are null for the other risk factor written in by the investigator as free text.
658H

Row 4:
Rows 1-4:
Rows 5-9:
Rows 5-8:
Row 9:
Row
1
2
3
4
5
6
7
8
9

STUDYID
ABC123
ABC123
ABC123
ABC123
ABC123
ABC123
ABC123
ABC123
ABC123

Row
1 (cont’d)
2 (cont’d)
3 (cont’d)
4 (cont’d)
5 (cont’d)
6 (cont’d)
7 (cont’d)
8 (cont’d)
9 (cont’d)

DOMAIN USUBJID MHSEQ MHTERM
MH
123101
1
ASTHMA
MH
123101
2
FREQUENT HEADACHES
MH
123101
3
BROKEN LEG
MH
123101
4
ISCHEMIC STROKE
MH
123101
5
DIABETES
MH
123101
6
HYPERCHOLESTEROLEMIA
MH
123101
7
HYPERTENSION
MH
123101
8
TIA
MH
123101
9
MATERNAL FAMILY HX OF STROKE

MHOCCUR MHBODSYS
Respiratory system disorders
Central and peripheral nervous system disorders
Musculoskeletal system disorders

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November 12, 2008

MHDECOD
Asthma
Headache
Bone fracture

MHSTDTC

MHCAT
GENERAL MEDICAL HISTORY
GENERAL MEDICAL HISTORY
GENERAL MEDICAL HISTORY
STROKE HISTORY
RISK FACTORS
RISK FACTORS
RISK FACTORS
RISK FACTORS
RISK FACTORS

MHSCAT
RESPIRATORY
CNS
OTHER

MHPRESP

Y
Y
Y
Y

MHENRF
DURING/AFTER
DURING/AFTER
BEFORE

2004-09-17T07:30
Y
Y
Y
N

CDISC SDTM Implementation Guide (Version 3.1.2)

6.2.4 PROTOCOL DEVIATIONS — DV
dv.xpt, Protocol Deviations — Events, Version 3.1.2. One record per protocol deviation per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char DV
Identifier Two-character abbreviation for the domain.
1923H

Core

References

Req
Req

Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4
SDTMIG 4.1.2.3
SDTM 2.2.4

Perm
Perm

SDTM 2.2.4
SDTM 2.2.4

Req

SDTM 2.2.2,
SDTMIG 4.1.3.6

659H

60H

USUBJID

Unique Subject Identifier

Char

DVSEQ

Sequence Number

Num

DVREFID
DVSPID

Reference ID
Char
Sponsor-Defined Identifier Char

DVTERM

Protocol Deviation Term

Char

Req
61H2

63H

TREATMENT DEVIATION.

DVDECOD

DVCAT
DVSCAT
EPOCH
DVSTDTC
DVENDTC

Protocol Deviation Coded Char *
Term

Category for Protocol
Deviation
Subcategory for Protocol
Deviation
Epoch

Char *

Start Date/Time of
Deviation
End Date/Time of
Deviation

Char ISO 8601

Synonym Controlled terminology for the name of the protocol deviation. Examples:
Qualifier SUBJECT NOT WITHDRAWN AS PER PROTOCOL, SELECTION
CRITERIA NOT MET, EXCLUDED CONCOMITANT MEDICATION,
TREATMENT DEVIATION.
Grouping Category of the protocol deviation criterion.
Qualifier
Grouping A further categorization of the protocol deviation.
Qualifier
Timing
Epoch associated with the start date/time of the deviation. Examples:
TREATMENT PHASE, SCREENING, and FOLLOW-UP.
Timing
Start date/time of deviation represented in ISO 8601 character format.

Char ISO 8601

Timing

Char *
Char *

Perm

SDTM 2.2.2,
SDTMIG 4.1.3.5
64H5

Perm

SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.5
SDTMIG 7.1.2
SDTM 2.2.5
SDTMIG 4.1.4.1
SDTM 2.2.5
SDTMIG 4.1.4.1
67H

Perm
68H

Perm
69H

Perm
670H

End date/time of deviation represented in ISO 8601 character format.

671H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.2.4.1 ASSUMPTIONS FOR PROTOCOL DEVIATIONS DOMAIN MODEL
1.

The DV domain is an Events model for collected protocol deviations and not for derived protocol deviations that are more likely to be part of analysis.
Events typically include what the event was, captured in --TERM (the topic variable), and when it happened (captured in its start and/or end dates). The
intent of the domain model is to capture protocol deviations that occurred during the course of the study (see ICH E3, Section 10.2). Usually these are
deviations that occur after the subject has been randomized or received the first treatment.

This domain should not be used to collect entry criteria information. Violated inclusion/exclusion criteria are stored in IE. The Deviations domain is for
more general deviation data. A protocol may indicate that violating an inclusion/exclusion criterion during the course of the study (after first dose) is a
protocol violation. In this case, this information would go into DV.

Additional Events Qualifiers
The following Qualifiers would generally not be used in DV: --PRESP, --OCCUR, --STAT, --REASND, --BODSYS, --LOC, --SEV, --SER, --ACN,
--ACNOTH, --REL, --RELNST, --PATT, --OUT, --SCAN, --SCONG, --SDISAB, --SDTH, --SHOSP, --SLIFE, --SOD, --SMIE, --CONTRT, --TOXGR.
6.2.4.2 EXAMPLES FOR PROTOCOL DEVIATIONS DOMAIN MODEL

Example 1
This is an example of data that was collected on a protocol-deviations CRF. The DVDECOD column is for controlled terminology whereas the DVTERM is free
text.
Rows 1 and 3: Show examples of a TREATMENT DEVIATION type of protocol deviation.
Row 2:

Shows an example of a deviation due to the subject taking a prohibited concomitant mediation.

Rows 4:

Shows an example of a medication that should not be taken during the study.

Row STUDYID DOMAIN USUBJID DVSEQ

DVTERM

DVDECOD

EPOCH

DVSTDTC

ABC123

123101

IVRS PROCESS DEVIATION - NO DOSE
CALL PERFORMED.

TREATMENT DEVIATION

TREATMENT PHASE

2003-09-21

ABC123

123103

DRUG XXX ADMINISTERED DURING
STUDY TREATMENT PERIOD

EXCLUDED CONCOMITANT
MEDICATION

TREATMENT PHASE

2003-10-30

ABC123

123103

VISIT 3 DOSE <15 MG

TREATMENT DEVIATION

TREATMENT PHASE

2003-10-30

ABC123

123104

TOOK ASPIRIN

PROHIBITED MEDS

TREATMENT PHASE

2003-11-30

1
2

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.2.5 CLINICAL EVENTS — CE
ce.xpt, Clinical Events — Events, Version 3.1.2. One record per event per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Type

Controlled Terms,
Codelist or
Format

Char
Char CE
1924H

Role

CDISC Notes

Identifier Unique identifier for a study.
Identifier Two-character abbreviation for the domain.

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
672H

673H

USUBJID

Unique Subject Identifier

Char

CESEQ

Sequence Number

Num

CEGRPID

Group ID

Char

SDTM 2.2.4
SDTMIG 2.1,
SDTMIG 4.1.2.6
SDTM 2.2.4
SDTM 2.2.4
67H

67H

CEREFID
CESPID

Reference ID
Char
Sponsor-Defined Identifier Char

CETERM

Reported Term for the
Clinical Event
Dictionary-Derived Term

CEDECOD

Identifier Internal or external identifier.
Perm
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm
explicit line identifier or defined in the sponsor‘s operational database.
Example: Line number on a CRF page.
Topic
Term for the medical condition or event. Most likely pre-printed on CRF. Req

Char

SDTM 2.2.2,
SDTMIG 4.1.3.6
SDTM 2.2.2,
SDTMIG 4.1.3.5
678H

Char *

Synonym Controlled terminology for the name of the clinical event. The sponsor
Qualifier is expected to provide the dictionary name and version used to

Perm
679H801

map the terms utilizing the define.xml external codelist attributes
CECAT

Category for Clinical Event Char *

CESCAT

Subcategory for Clinical
Event
Clinical Event PreSpecified

CEPRESP

Char *
Char (NY)
1925H

Grouping
Qualifier
Grouping
Qualifier
Record
Qualifier

Used to define a category of related records.

Perm

A further categorization of the condition or event.

Perm

Record
Qualifier
Record
Qualifier

Used when the occurrence of specific events is solicited to indicate
Perm
whether or not a clinical event occurred. Values are null for spontaneously
reported events.
The status indicates that a question from a pre-specified list was not
Perm
answered.

Record
Qualifier

Describes the reason clinical event data was not collected. Used in
conjunction with CESTAT when value is NOT DONE.

SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.6
SDTM 2.2.2,
SDTMIG 4.1.2.7
SDTMIG 4.1.5.7
SDTM 2.2.2,
SDTMIG 4.1.5.7
682H

683H

Used to indicate whether the Event in CETERM was pre-specified. Value Perm
is Y for pre-specified events, null for spontaneously reported events.
684H5

68H

CEOCCUR

CESTAT

Clinical Event Occurrence Char (NY)
1926H

Completion Status

Char (ND)
1927H

687H

SDTM 2.2.2,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.2,
SDTMIG 4.1.5.1
SDTMIG 4.1.5.7
68H

689H

CEREASND

Reason Clinical Event Not Char
Collected

Perm
690H

691H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Body System or Organ
Class

Controlled Terms,
Codelist or
Role
Format
Char *
Record
Qualifier

CESEV

Severity/Intensity

Char *

CEDTC

Date/Time of Event
Char ISO 8601
Collection
Start Date/Time of Clinical Char ISO 8601
Event

Variable
Name
CEBODSYS

CESTDTC

Variable Label

Type

Record
Qualifier
Timing

CDISC Notes

Core

Dictionary-derived. Body system or organ class that is involved in an
Perm
event or measurement from a standard hierarchy (e.g., MedDRA). When
using a multi-axial dictionary such as MedDRA, this should contain the
SOC used for the sponsor‘s analyses and summary tables which may not
necessarily be the primary SOC.
The severity or intensity of the event. Examples: MILD, MODERATE, Perm
SEVERE
Perm

References
SDTM 2.2.2,
SDTMIG 4.1.3.5
692H34

SDTM 2.2.2,
SDTM 2.2.5,
SDTMIG 4.1.4.1
SDTM 2.2.5,
SDTMIG 4.1.4.1;
SDTMIG 4.1.4.2
SDTM 2.2.5,
SDTMIG 4.1.4.1;
SDTMIG 4.1.4.2
SDTM 2.2.5,
SDTMIG 4.1.4.4
695H

Timing

Perm
69H

697H

CEENDTC

End Date/Time of Clinical Char ISO 8601
Event

Timing

Study Day of Event
Collection

Timing

Perm
698H

69H

CEDY

CESTRF

CEENRF

CESTRTPT
CESTTPT

CEENRTPT

CEENTPT

Num

Start Relative to Reference Char (STENRF)
Period

Timing

End Relative to Reference Char (STENRF)
Period

Timing

1928H

192H

Start Relative to Reference Char BEFORE, AFTER, Timing
Time Point
COINCIDENT, U
Start Reference Time Point Char
Timing

End Relative to Reference Char BEFORE, AFTER, Timing
Time Point
COINCIDENT,
ONGOING, U
End Reference Time Point Char
Timing

1. Study day of clinical event collection, measured as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.
Describes the start of the clinical event relative to the sponsor-defined
reference period. The sponsor-defined reference period is a continuous
period of time defined by a discrete starting point and a discrete ending
point (represented by RFSTDTC and RFENDTC in Demographics).
Describes the end of the event relative to the sponsor-defined reference
period. The sponsor-defined reference period is a continuous period of
time defined by a discrete starting point and a discrete ending point
(represented by RFSTDTC and RFENDTC in Demographics).
Identifies the start of the observation as being before or after the reference
time point defined by variable CESTTPT.
Description or date/time in ISO 8601 character format of the sponsordefined reference point referred to by --STRTPT. Examples:
"2003-12-15" or "VISIT 1".
Identifies the end of the event as being before or after the reference time
point defined by variable CEENTPT.

Perm
70H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.7
701H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.7
702H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.7
SDTM 2.2.5,
SDTMIG 4.1.4.7
703H

Perm
704H

Perm

Description or date/time in ISO 8601 character format of the reference
Perm
point referred to by CEENRTPT. Examples: "2003-12-25" or "VISIT 2".

SDTM 2.2.5,
SDTMIG 4.1.4.7
705H

SDTM 2.2.5,
SDTMIG 4.1.4.7
706H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.2.5.1 ASSUMPTIONS FOR CLINICAL EVENTS DOMAIN MODEL
1.

The intent of the domain model is to capture clinical events of interest that would not be classified as adverse events. The data may be data about
episodes of symptoms of the disease under study (often known as signs and symptoms), or about events that do not constitute adverse events in
themselves, though they might lead to the identification of an adverse event. For example, in a study of an investigational treatment for migraine
headaches, migraine headaches may not be considered to be adverse events per protocol. The occurrence of migraines or associated signs and symptoms
might be reported in CE. Other studies might track the occurrence of specific events as efficacy endpoints. For example, in a study of an investigational
treatment for prevention of ischemic stroke, all occurrences of TIA, stroke and death might be captured as clinical events and assessed as to whether
they meet endpoint criteria. Note that other information about these events may also be reported in other datasets. For example, the event leading to
death would be reported in AE and death would also be a reason for study discontinuation in DS.

CEOCCUR and CEPRESP are used together to indicate whether the event in CETERM was pre-specified, and whether it occurred. CEPRESP can be
used to separate records that correspond to probing questions for pre-specified events from those that represent spontaneously reported events, while
CEOCCUR contains the responses to such questions. The table below shows how these variables are populated in various situations.
Situation
Spontaneously reported event occurred
Pre-specified event occurred
Pre-specified event did not occur
Pre-specified event has no response

Value of
CEPRESP

Value of
CEOCCUR

Y
Y
Y

Y
N

Value of CESTAT

NOT DONE

The collection of write-in events on a Clinical Events CRF should be considered with caution. Sponsors must ensure that all adverse events are recorded
in the AE domain.

Timing Variables
a. Relative timing assessments "Prior" or ―Ongoing‖ are common in the collection of Clinical Event information. CESTRF or CEENRF may be used
when this timing assessment is relative to the study reference period for the subject represented in the Demographics dataset (RFENDTC).
CESTRTPT with CESTTPT, and/or CEENRTPT with CEENTPT may be used when "Prior" or "Ongoing" are relative to specific dates other than
the start and end of the study reference period. See Section 4.1.4.7.
b. Additional Timing variables may be used when appropriate.
70H

Additional Events Qualifiers
The following Qualifiers would generally not be used in CE: --SER, --ACN, --ACNOTH, --REL, --RELNST, --OUT, --SCAN, --SCONG,
--SDISAB, --SDTH, --SHOSP, --SLIFE, --SOD, --SMIE.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.2.5.2 EXAMPLES FOR CLINICAL EVENTS DOMAIN MODEL
Example 1
Assumptions:
 CRF data are collected about pre-specified events that, in the context of this study, are not reportable as Adverse Events.
 The data being collected includes ―event-like‖ timing and other Qualifiers.
 Data are collected about pre-specified clinical events in a log independent of visits, rather than in a visit-based CRF module.
 Note that data collected is the start date of the event, which is ―event-like‖ data.
 No "yes/no" data on the occurrence of the event is collected.
CRF:
Record start dates of any of the following signs that occur.
Clinical Sign
Start Date
Rash
Wheezing
Edema
Conjunctivitis

Data:
Rows 1-3: Show records for clinical events for which start dates were recorded. Since conjunctivitis was not observed, no start date was recorded and there is no
CE record.
Row
1
2
3

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STUDYID
ABC123
ABC123
ABC123

DOMAIN
CE
CE
CE

USUBJID
123
123
123

CESEQ
1
2
3

CETERM CEPRESP
Rash
Y
Wheezing
Y
Edema
Y

CEOCCUR
Y
Y
Y

CESTDTC
2006-05-03
2006-05-03
2006-05-06

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 2
Assumptions:
 CRF includes both questions about pre-specified clinical events (events not reportable as AEs in the context of this study) and spaces for the investigator
to write in additional clinical events.
 Note that data being collected are start and end dates, which are "event-like, " and severity, which is a Qualifier in the Events general observation class.
CRF:
Event

Yes

Date Started

Date Ended

Severity

Nausea
Vomit
Diarrhea
Other, Specify

Data:
Row 1:
Row 2:
Row 3:
Row 4:

Shows a record for a response to the pre-specified clinical event "Nausea." The CEPRESP value of ―Y‖ indicates that there was a probing question,
and the response to the probe (CEOCCUR) was "Yes."
Shows a record for a response to the pre-specified clinical event "Vomit." The CEPRESP value of ―Y‖ indicates that there was a probing question,
and the response to the question (CEOCCUR) was "No‖.
Shows a record for the pre-specified clinical event "Diarrhea." A value of Y for CEPRESP indicates it was pre-specified. The CESTAT value of NOT
DONE indicates that there was either 1) no probing question (investigator error) or a probing question with no response.
Shows a record for a write-in Clinical Event recorded in the "Other, Specify" space. Because this event was not pre-specified, CEPRESP and
CEOCCUR are null (Section 4.1.2.7 further information on populating the Topic variable when ―Other, specify‖ is used on the CRF).
708H

Row STUDYID DOMAIN USUBJID CESEQ
ABC123
CE
123
1
1
ABC123
CE
123
2
2
ABC123
CE
123
3
3
4

ABC123

123

CETERM
CEPRESP CEOCCUR CESTAT
CESEV
CESTDTC CEENDTC
NAUSEA
Y
Y
MODERATE 2005-10-12 2005-10-15
VOMIT
Y
N
DIARRHEA
Y
NOT DONE
SEVERE HEAD
SEVERE
2005-10-09 2005-10-11
PAIN

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.3 FINDINGS
6.3.1 ECG TEST RESULTS — EG
eg.xpt, ECG — Findings, Version 3.1.2. One record per ECG observation per time point per visit per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char EG
Identifier Two-character abbreviation for the domain.
1930H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
709H

710H

USUBJID

Unique Subject Identifier Char

EGSEQ

Sequence Number

Num

EGGRPID

Group ID

Char

EGREFID

ECG Reference ID

Char

EGSPID

Sponsor-Defined Identifier Char

Req
71H2

a Req
Perm

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
713H

Perm
714H

EGTESTCD

EGTEST

ECG Test or Examination Char (EGTESTCD)
Short Name
716H

ECG Test or Examination Char (EGTEST)
Name
721H

Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm
explicit line identifier or defined in the sponsor's operational database.
Example: Line number from the ECG page.
Topic
Short name of the measurement, test, or examination described in
Req
EGTEST. It can be used as a column name when converting a dataset
from a vertical to a horizontal format. The value in EGTESTCD cannot be
longer than 8 characters, nor can it start with a number (e.g., ―1TEST‖).
EGTESTCD cannot contain characters other than letters, numbers, or
underscores. Examples :PRMEAN, QTMEAN
Synonym Verbatim name of the test or examination used to obtain the measurement Req
Qualifier or finding. The value in EGTEST cannot be longer than 40 characters.
Examples: Summary (Mean) PR Duration, Summary (Mean) QT Duration
715H

SDTM 2.2.3,
SDTMIG 4.1.1.9,
SDTMIG 4.1.2.1,
SDTMIG 4.1.5.5
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3
71H8

719H

720H

72H

723H

724H

725H

EGCAT

Category for ECG

Char *

EGSCAT

Subcategory for ECG

Char *

EGPOS

ECG Position of Subject

Char (POSITION)

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November 12, 2008

728H

Grouping
Qualifier
Grouping
Qualifier
Record
Qualifier

Used to categorize ECG observations across subjects. Examples:
MEASUREMENT, FINDING, INTERVAL
A further categorization of the ECG.

Perm

Position of the subject during a measurement or examination. Examples:
SUPINE, STANDING, SITTING.

Perm

726H

Perm
72H

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
EGORRES

EGORRESU

Result or Finding in
Original Units

Controlled
Type Terms, Codelist
Role
or Format
Char
Result
Qualifier

Original Units

Char (UNIT)

Variable Label

731H

CDISC Notes

Core

References

Result of the ECG measurement or finding as originally received or
Exp
collected. Examples of expected values are 62 or 0.151 when the result is
an interval or measurement, or ―ATRIAL FIBRILLATION‖ or ―QT
PROLONGATION‖ when the result is a finding.

SDTM 2.2.3,
SDTMIG 4.1.5.1

Variable
Qualifier

Original units in which the data were collected. The unit for EGORRES.
Examples: sec or msec.

Result
Qualifier

Contains the result value for all findings, copied or derived from
Exp
EGORRES in a standard format or standard units. EGSTRESC should
store all results or findings in character format; if results are numeric, they
should also be stored in numeric format in EGSTRESN. For example, if a
test has results of ―NONE‖, ―NEG‖, and ―NEGATIVE‖ in EGORRES
and these results effectively have the same meaning, they could be
represented in standard format in EGSTRESC as ―NEGATIVE‖. For
other examples, see general assumptions. Additional examples of result
data: SINUS BRADYCARDIA, ATRIAL FLUTTER, ATRIAL
FIBRILLATION.
Used for continuous or numeric results or findings in standard format;
Perm
copied in numeric format from EGSTRESC. EGSTRESN should store all
numeric test results or findings.
Standardized unit used for EGSTRESC or EGSTRESN.
Perm

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTM 2.2.3,
SDTMIG 4.1.3.6,
SDTMIG 4.1.5.1

729H30

Perm
732H

73H

EGSTRESC

EGSTRESN

EGSTRESU

Character Result/Finding Char (EGSTRESC)
in Std Format
734H

Numeric Result/Finding in Num
Standard Units
Standard Units

Result
Qualifier

Char (UNIT)
738H

Variable
Qualifier

735H

736H

SDTM 2.2.3,
SDTMIG 4.1.5.1
73H

Describes why a measurement or test was not performed. Examples:
BROKEN EQUIPMENT or SUBJECT REFUSED. Used in conjunction
with EGSTAT when value is NOT DONE.
File name and path for the external ECG Waveform file.

Perm

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3

Name or identifier of the laboratory or vendor who provided the test
results.
The lead used for the measurement, examples, V1, V6, aVR, I, II, III.

Perm

SDTM 2.2.3

Perm

SDTM 2.2.3,

Method of the ECG test. Examples: 12 LEAD STANDARD.

Perm

739H

740H

EGSTAT

Completion Status

Char (ND)
193H

Record
Qualifier

Used to indicate an ECG was not done, or an ECG measurement was not Perm
taken. Should be null if a result exists in EGORRES.
741H

742H

743H

EGREASND Reason ECG Not
Performed

Char

EGXFN

ECG External File Name Char

EGNAM

Vendor Name

EGLOC

Lead Location Used for
Measurement
EGMETHOD Method of ECG Test

Record
Qualifier

Char
Char (LOC)
746H

Char (EGMETHOD)
749H

Record
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier

74H

745H

SDTMIG 4.1.1.9
74H

SDTM 2.2.3,
SDTMIG
Appendix C1
750H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
EGBLFL

Variable Label
Baseline Flag

Controlled
Type Terms, Codelist
Role
or Format
Char (NY)
Record
Qualifier
1932H

CDISC Notes
Indicator used to identify a baseline value. The value should be ―Y‖ or
null.

Core
Exp

References
SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.4
751H

752H

EGDRVFL

EGEVAL

VISITNUM

Derived Flag

Evaluator

Visit Number

Char (NY)
193H

Char *

Num

Record
Qualifier

Timing

Used to indicate a derived record. The value should be Y or null. Records which Perm
represent the average of other records, or that do not come from the CRF, or are not
as originally collected or received are examples of records that would be derived
for the submission datasets. If EGDRVFL=Y, then EGORRES could be null, with
EGSTRESC, and (if numeric) EGSTRESN having the derived value.
Role of the person who provided the evaluation. Used only for results that Perm
are subjective (e.g., assigned by a person or a group). Should be null for
records that contain collected or derived data. Examples:
INVESTIGATOR, ADJUDICATION COMMITTEE, VENDOR.
1. Clinical encounter number.
Exp
2. Numeric version of VISIT, used for sorting.
753H

754H

75H

756H

SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.2,
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.10
75H

758H

VISIT

Visit Name

Char

Timing

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

759H

760H

VISITDY

Planned Study Day of
Visit

Num

Date/Time of ECG

Char ISO 8601

Timing

761H

762H

EGDTC

Timing

Date of ECG.

Exp
763H

764H

765H

EGDY

EGTPT

Study Day of ECG

Num

Planned Time Point Name Char

EGTPTNUM Planned Time Point
Number
EGELTM
Planned Elapsed Time
from Time Point Ref

Page 126
November 12, 2008

Timing

Num

Timing

Char ISO 8601

Timing

1. Study day of the ECG, measured as integer days.
Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.
1. Text Description of time when measurement should be taken.
Perm
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See EGTPTNUM and EGTPTREF.
Examples: Start, 5 min post.
Numerical version of EGTPT to aid in sorting.
Perm
76H

768H

769H

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTMIG 4.1.4.10
70H

Planned elapsed time (in ISO 8601) relative to a fixed time point reference
Perm
(EGTPTREF). Not a clock time or a date time variable. Represented as an ISO
8601 duration. Examples: ―-PT15M‖ to represent the period of 15 minutes
prior to the reference point indicated by EGTPTREF, or ―PT8H‖ to represent
the period of 8 hours after the reference point indicated by EGTPTREF.
71H

72H

CDISC SDTM Implementation Guide (Version 3.1.2)
Controlled
Type Terms, Codelist
Role
or Format
Char
Timing

Variable
Name

Variable Label

EGTPTREF

Time Point Reference

EGRFTDTC

Date/Time of Reference
Time Point

Char ISO 8601

Timing

CDISC Notes

Core

References

Name of the fixed reference point referred to by EGELTM, EGTPTNUM, Perm
and EGTPT. Examples: PREVIOUS DOSE, PREVIOUS MEAL.
Date/time of the reference time point, EGTPTREF.
Perm

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.10
73H

74H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.1.1 ASSUMPTIONS FOR ECG TEST RESULTS DOMAIN MODEL
1.

EG Definition: CRF data that captures interval measurements and summary information from an ECG. This domain captures ECG data collected on
the CRF or received from a central provider or vendor.

EGREFID is intended to store an identifier (e.g., UUID) for the associated ECG tracing. EGFXN is intended to store the name of and path to the ECG
waveform file when it is submitted.

The method for QT interval correction is specified in the test name by controlled terminology: EGTESTCD = QTCF and EGTEST = QTcF for
Fridericia‘s Correction Formula; EGTESTCD=QTCB and EGTEST = QTcB for Bazett's Correction Formula.

EGNRIND can be added to indicate where a result falls with respect to reference range defined by EGORNRLO and EGORNRHI. Examples: HIGH,
LOW. Clinical significance would be represented as described in Section 4.1.5.5 as a record in SUPPEG with a QNAM of EGCLSIG (see also ECG
Example 1 below).
75H

When QTCF and QTCB are derived by the sponsor, the derived flag (EGDRVFL) is set to Y. However, when the QTCF or QTCB is received from a
central provider or vendor, the value would go into EGORRES and EGDRVFL would be null (See Section 4.1.1.8.1).
76H

The following Qualifiers would not generally be used in EG: --MODIFY, --BODSYS, --SPEC, --SPCCND, --FAST, --SEV. It is recommended that
--LOINC not be used.
6.3.1.2 EXAMPLES FOR ECG TEST RESULTS DOMAIN MODEL

Example 1
Rows 1-6:
Row 1:

Show how ECG measurements are represented.
Shows a measurement of ventricular rate. The Supplemental Qualifier record related to this EG record, Row 1 in the SUPPEG dataset, has
QNAM = EGCLSIG and QVAL = "N". This indicates that the ventricular rate of 62 bpm was assessed as not being clinically significant. See
Section 4.1.5.5 for more on clinical significance.
Show the data in original units of measure in EGORRES, EGSTRESC, and EGSTRESN. See Section 4.1.5.1 for additional examples for the
population of Result Qualifiers.
The TEST " Summary (Mean) PR Duration ―has a result of 0.15 sec. The Supplemental Qualifier record related to this EG record, Row 2 in the
SUPPEG dataset, has QNAM = CLSIG and QVAL = "Y". This indicates that the PR interval of 0.15 sec was assessed as being clinically
significant. See Section 4.1.5.5 for more on clinical significance.
Show how EGCAT could be used to group the intervals and the findings.
7H

Rows 2-4:
Row 2:

78H

79H

Rows 2-10:

Page 127
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Rows 5-6:
Rows 7-10:
Row 11:
Row 12:

Show QTCB and QTCF. The data shows a ―Y‖ in the EGDRVFL column since these results are derived by the sponsor in this example. Note that
EGORRES is null for these derived records.
Show how ECG findings are represented.
Shows a way of representing technical problems that are important to the overall understanding of the ECG, but which are not truly findings or
interpretations.
The TEST "Interpretation" (i.e., the interpretation of the ECG strip as a whole), is "ABNORMAL ".

eg.xpt
Row STUDYID

DOMAIN

USUBJID

EGSEQ

EGCAT

EGREFID

EGTESTCD

XYZ

XYZ-US-701-002

MEASUREMENT

334PT89

HRMEAN

XYZ

XYZ-US-701-002

INTERVAL

334PT89

PRMEAN

XYZ

XYZ-US-701-002

INTERVAL

334PT89

QRSDUR

XYZ

XYZ-US-701-002

INTERVAL

334PT89

QTMEAN

XYZ

XYZ-US-701-002

INTERVAL

334PT89

QTCB

XYZ

XYZ-US-701-002

INTERVAL

334PT89

QTCF

7
8
9

XYZ
XYZ
XYZ

EG
EG
EG

XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002

7
8
9

FINDING
FINDING
FINDING

334PT89
334PT89
334PT89

RHYRATE
RHYRATE
QTABN

XYZ

XYZ-US-701-002

FINDING

334PT89

VCABN

XYZ

XYZ-US-701-002

334PT89

XYZ

XYZ-US-701-002

334PT89

Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)

EGSTRESC
62
150
103
406
469
446
ATRIAL FIBRILLATION
ATRIAL FLUTTER
PROLONGED QT

Page 128
November 12, 2008

EGSTRESN
62
150
103
406
469
446

EGSTRESU
BEATS/MIN
msec
msec
msec
msec
msec

EGTEST
Summary (Mean) Heart Rate
Summary (Mean) PR Duration
Summary (Mean) QRS
Duration
Summary (Mean) QT Duration
QTcB – Bazett's Correction
Formula

EGPOS

EGORRES

SUPINE

0.15

sec

SUPINE

0.103

sec

SUPINE

0.406

sec

SUPINE

QTcF – Fridericia's Correction
Formula
SUPINE
Rhythm and Rate
Rhythm and Rate
QT Abnormalities
Ventricular Conduction
Abnormalities

SUPINE
SUPINE
SUPINE

TECHPROB

Technical Problems

SUPINE

INTP

Interpretation

SUPINE

EGXFN
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml
PQW436789-07.xml

EGNAM
Test Lab
Test Lab
Test Lab
Test Lab
Test Lab
Test Lab
Test Lab
Test Lab
Test Lab

EGORRESU
BEATS/MIN

EGDRVFL

Y
Y

EGEVAL

SUPINE

VISITNUM
1
1
1
1
1
1
1
1
1

ATRIAL FIBRILLATION
ATRIAL FLUTTER
PROLONGED QT
LEFT VENTRICULAR
HYPERTROPHY
INCORRECT ELECTRODE
PLACEMENT
ABNORMAL
VISIT
Screening 1
Screening 1
Screening 1
Screening 1
Screening 1
Screening 1
Screening 1
Screening 1
Screening 1

EGDTC
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58
2003-04-15T11:58

EGDY
-36
-36
-36
-36
-36
-36
-36
-36
-36

CDISC SDTM Implementation Guide (Version 3.1.2)
Row
10 (cont)
11 (cont)

EGSTRESC
LEFT VENTRICULAR
HYPERTROPHY
INCORRECT ELECTRODE
PLACEMENT

EGSTRESN

EGSTRESU

EGXFN

EGNAM

PQW436789-07.xml
PQW436789-07.xml

EGDRVFL

VISITNUM

VISIT

EGDTC

EGDY

Test Lab

Screening 1

2003-04-15T11:58

-36

Test Lab

Screening 1

2003-04-15T11:58

-36

Screening 1

2003-04-15T11:58

-36

PRINCIPAL
INVESTIGA
TOR

ABNORMAL

12 (cont)

EGEVAL

suppeg.xpt
Row 1: Shows that the record in the EG dataset with value of EGSEQ of 1 has Supplemental Qualifier record indicating that the ventricular rate of 62 bpm was
assessed as not being clinically significant.
Row 2: Shows that the record in the EG dataset with value of EGSEQ of 2 has Supplemental Qualifier record indicating that the PR interval of 0.15 sec was
assessed as being clinically significant.
Row
1
2

STUDYID RDOMAIN
USUBJID
IDVAR
XYZ
EG
XYZ-US-701-002 EGSEQ
XYZ
EG
XYZ-US-701-002 EGSEQ

IDVARVAL QNAM
QLABEL
1
EGCLSIG Clinically Significant
2
EGCLSIG Clinically Significant

QVAL
N
Y

QORIG QEVAL
CRF
CRF

Example 2
Example 2 shows results for one subject across multiple visits where only the overall assessment was collected. In addition the ECG done at Visit 4 was read by
the principal investigator and a cardiologist. In this example the EGGRPID is the same number and the EGSEQ increments by one.
Rows 1-5:
Row 2:
Row 3:
Rows 4-5:

Show that when an interpretation is collected the evaluator is stored in EGEVAL.
Shows the record selected as Baseline.
Shows a date/time in ISO 8601 representation where both the date and time were collected.
Show where EGGRPID is used to group related results.

Row STUDYID DOMAIN
USUBJID
EGSEQ EGGRPID EGTESTCD EGTEST
ABC
EG
ABC-99-CA-456
1
1
INTP
Interpretation
1
ABC
EG
ABC-99-CA-456
2
2
INTP
Interpretation
2
ABC
EG
ABC-99-CA-456
3
3
INTP
Interpretation
3
ABC
EG
ABC-99-CA-456
4
4
INTP
Interpretation
4
ABC
EG
ABC-99-CA-456
5
4
INTP
Interpretation
5
Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)

EGBLFL
Y

EGEVAL
PRINCIPAL INVESTIGATOR
PRINCIPAL INVESTIGATOR
PRINCIPAL INVESTIGATOR
PRINCIPAL INVESTIGATOR
CARDIOLOGIST

VISITNUM
1
2
3
4
4

VISIT
SCREEN I
SCREEN II
DAY 10
DAY 15
DAY 15

EGPOS
SUPINE
SUPINE
SUPINE
SUPINE
SUPINE

VISITDY
-2
-1
10
15
15

EGORRES
NORMAL
ABNORMAL
ABNORMAL
ABNORMAL
ABNORMAL

EGDTC
2003-11-26
2003-11-27
2003-12-07T09:02
2003-12-12
2003-12-12

EGSTRESC EGSTRESN
NORMAL
ABNORMAL
ABNORMAL
ABNORMAL
ABNORMAL
EGDY
-2
-1
10
15
15

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.2 INCLUSION/EXCLUSION CRITERIA NOT MET — IE
ie.xpt, Inclusion/Exclusion Criteria Not Met — Findings, Version 3.1.2. One record per inclusion/exclusion criterion not met per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char IE
Identifier Two-character abbreviation for the domain.
1934H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
780H

781H

USUBJID

Unique Subject Identifier Char

IESEQ

Sequence Number

IESPID

Sponsor-Defined Identifier Char

IETESTCD

IETEST

Inclusion/Exclusion
Criterion Short Name

Inclusion/Exclusion
Criterion

Num

Char *

Char

Identifier Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. May be any valid number.
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an
explicit line identifier or defined in the sponsor‘s operational database.
Example: Inclusion or Exclusion criteria number from CRF.
Topic
Short name of the criterion described in IETEST. The value in
IETESTCD cannot be longer than 8 characters, nor can it start with a
number (e.g.‖1TEST‖). IETESTCD cannot contain characters other than
letters, numbers, or underscores. Examples: IN01, EX01.
Synonym Verbatim description of the inclusion or exclusion criterion that was the
Qualifier exception for the subject within the study. IETEST cannot be longer than
200 characters.

Req

Grouping Used to define a category of related records across subjects.
Qualifier

Req

782H3

Req
Perm

SDTM 2.2.4,
SDTMIG 4.1.2.6
784H

Req
785H

SDTM 2.2.3,
SDTMIG 4.1.1.9
SDTMIG 4.1.2.1

Req

78H

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
SDTM 2.2.3,
SDTMIG 4.1.2.6,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.2.6
78H

789H

790H

IECAT

Inclusion/Exclusion
Category

Char (IECAT)
1935H

791H

792H

IESCAT

IEORRES

Inclusion/Exclusion
Subcategory

Char *

I/E Criterion Original
Result

Char (NY)
1936H

Grouping A further categorization of the exception criterion. Can be used to
Perm
Qualifier distinguish criteria for a sub-study or for to categorize as a major or minor
exceptions. Examples: MAJOR, MINOR.
Result
Original response to Inclusion/Exclusion Criterion question. Inclusion or Req
Qualifier Exclusion criterion met?
793H

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.6,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
794H5

796H

IESTRESC

I/E Criterion Result in Std Char (NY)
Format
1937H

Result
Qualifier

Response to Inclusion/Exclusion criterion result in standard format.

Req
79H

798H

79H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
VISITNUM

Variable Label
Visit Number

Controlled
Type Terms, Codelist
Role
or Format
Num
Timing

CDISC Notes

Core

1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Perm

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

References
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.2,
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
80H

801H

VISIT

Visit Name

Char

Timing

802H

803H

VISITDY

Planned Study Day of
Visit

Num

Date/Time of Collection

Char ISO 8601

Timing

804H

805H

IEDTC

Timing

Perm
806H

807H

80H

IEDY

Study Day of Collection

Num

Timing

1. Study day of collection of the inclusion/exclusion exceptions, measured Perm
as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.
809H

810H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.2.1 ASSUMPTIONS FOR INCLUSION/EXCLUSION CRITERIA NOT MET DOMAIN MODEL
1.

IE Definition:
CRF data that captures inclusion and exclusion criteria exceptions per subject. All inclusion or exclusion criteria that are violated, should be stored
here, even if a sponsor has granted a waiver or if the subject was admitted by mistake. In cases where a CRF may allow a response of ―Not
Applicable‖ and this is checked, no criteria were violated, so these records would not be in IE.

The intent of the domain model is to collect responses to only those criteria that the subject did not meet, and not the responses to all criteria. The
complete list of Inclusion/Exclusion criteria can be found in the TI trial inclusion/exclusion criteria dataset described in Section 7.5.
81H

This domain should be used to document the exceptions to inclusion or exclusion criteria at the time that eligibility for study entry is determined
(e.g., at the end of a run-in period or immediately before randomization). This domain should not be used to collect protocol deviations/violations
incurred during the course of the study, typically after randomization or start of study medication. See Section 6.2.4.1 for the DV events domain
model that is used to submit protocol deviations/violations.
812H

IETEST is to be used only for the verbatim description of the inclusion or exclusion criteria. If the text is <200 characters, it goes in IETEST; if the
text is > 200 characters, put meaningful text in IETEST and describe the full text in the study metadata. See section 4.1.5.3.2 for further
information.
813H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
5.

The following Qualifiers would not generally be used in IE: --MODIFY, --POS, --BODSYS, --ORRESU, --ORNRLO, --ORNRHI, --STRESN,
--STRESU, --STNRLO, --STNRHI, --STNRC, --NRIND, --RESCAT, --XFN, --NAM, --LOINC, --SPEC, --SPCCND, --LOC, --METHOD,
--BLFL, --FAST, --DRVFL, --TOX, --TOXGR, --SEV, --STAT.

6.3.2.2 EXAMPLES FOR INCLUSION/EXCLUSION NOT MET DOMAIN MODEL
This example shows records for three subjects; one with 2 inclusion/exclusion exceptions, and the others with one exception each. Subject XYZ-0007 failed
exclusion criterion number 17 and inclusion criterion 3, but was included in the trial. The other two subjects each failed inclusion criterion number 3, but were
also included in the trial.
Row STUDYID DOMAIN USUBJID IESEQ IESPID
XYZ
IE
XYZ-0007
1
17
1
XYZ
IE
XYZ-0007
2
3
2
XYZ
IE
XYZ-0047
1
3
3
XYZ
IE
XYZ-0096
1
3
4
Row VISITNUM
1
1 (cont)
1
2 (cont)
1
3 (cont)
1
4 (cont)

Page 132
November 12, 2008

VISIT
WEEK -8
WEEK -8
WEEK -8
WEEK -8

VISITDY
-56
-56
-56
-56

IETESTCD
EXCL17
INCL03
INCL03
INCL03

IEDTC
1999-01-10
1999-01-10
1999-01-12
1999-01-13

IETEST
Ventricular Rate
Acceptable mammogram from local radiologist?
Acceptable mammogram from local radiologist?
Acceptable mammogram from local radiologist?

IECAT
EXCLUSION
INCLUSION
INCLUSION
INCLUSION

IEORRES
Y
N
N
N

IESTRESC
Y
N
N
N

IEDY
-58
-58
-56
-55

CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.3 LABORATORY TEST RESULTS — LB
lb.xpt, Labs — Findings, Version 3.1.2. One record per lab test per time point per visit per subject, Tabulation
Variable Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char LB
Identifier Two-character abbreviation for the domain.
1938H

References

Core
Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG Appendix
C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
814H

815H

USUBJID

Unique Subject Identifier Char

LBSEQ

Sequence Number

Num

LBGRPID

Group ID

Char

LBREFID

Specimen ID

Char

LBSPID

Sponsor-Defined
Identifier

Char

Identifier

Req
816H7

Req

Perm SDTM 2.2.4,
SDTMIG 4.1.2.6
Perm SDTM 2.2.4,
SDTMIG 4.1.2.6
Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm SDTM 2.2.4,
explicit line identifier or defined in the sponsor‘s operational database.
SDTMIG 4.1.2.6
Example: Line number on the Lab page.
Short name of the measurement, test, or examination described in
Req SDTM 2.2.3,
LBTEST. It can be used as a column name when converting a dataset from
SDTMIG 4.1.1.9
a vertical to a horizontal format. The value in LBTESTCD cannot be
SDTMIG 4.1.2.1,
longer than 8 characters, nor can it start with a number (e.g.‖1TEST‖).
SDTMIG Appendix
LBTESTCD cannot contain characters other than letters, numbers, or
C1
underscores. Examples: ALT, LDH.
Verbatim name of the test or examination used to obtain the measurement Req SDTM 2.2.3,
or finding. Note any test normally performed by a clinical laboratory is
SDTMIG 4.1.2.1,
considered a lab test. The value in LBTEST cannot be longer than 40
SDTMIG 4.1.2.4,
characters. Examples: Alanine Aminotransferase, Lactate Dehydrogenase.
SDTMIG 4.1.5.3.1
SDTMIG Appendix
C1
Used to define a category of related records across subjects. Examples:
Exp SDTM 2.2.3,
such as HEMATOLOGY, URINALYSIS, CHEMISTRY.
SDTMIG 4.1.2.6
A further categorization of a test category such as DIFFERENTIAL,
Perm SDTM 2.2.3,
COAGULATON, LIVER FUNCTION, ELECTROLYTES.
SDTMIG 4.1.2.6
Result of the measurement or finding as originally received or collected. Exp SDTM 2.2.3,
SDTMIG 4.1.5.1
81H

819H

LBTESTCD

820H

Lab Test or Examination Char (LBTESTCD)
Short Name
193H

Topic

821H

823H

824H

LBTEST

Lab Test or Examination Char (LBTEST)
Name
1940H

Synonym
Qualifier

825H

826H

827H

82H

LBCAT

Category for Lab Test

Char *

LBSCAT

Subcategory for Lab Test Char *

LBORRES

Result or Finding in
Original Units

Char

Grouping
Qualifier
Grouping
Qualifier
Result
Qualifier

829H

830H

831H

832H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Variable Name
LBORRESU

Variable Label
Original Units

Controlled
Type Terms, Codelist
Role
or Format
Char (UNIT)
Variable
Qualifier
83H

CDISC Notes

References

Core

Original units in which the data were collected. The unit for LBORRES.
Example: g/L.

Exp

Lower end of reference range for continuous measurements in original
units. Should be populated only for continuous results.
Upper end of reference range for continuous measurements in original
units. Should be populated only for continuous results.
Contains the result value for all findings, copied or derived from
LBORRES in a standard format or standard units. LBSTRESC should
store all results or findings in character format; if results are numeric, they
should also be stored in numeric format in LBSTRESN. For example, if a
test has results ―NONE‖, ―NEG‖, and ―NEGATIVE‖ in LBORRES and
these results effectively have the same meaning, they could be represented
in standard format in LBSTRESC as ―NEGATIVE‖. For other examples,
see general assumptions.
Used for continuous or numeric results or findings in standard format;
copied in numeric format from LBSTRESC. LBSTRESN should store all
numeric test results or findings.
Standardized unit used for LBSTRESC or LBSTRESN.

Exp

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTM 2.2.3

Exp

SDTM 2.2.3

Exp

SDTM 2.2.3,
SDTMIG 4.1.5.1

834H

835H

LBORNRLO
LBORNRHI
LBSTRESC

LBSTRESN

LBSTRESU

Reference Range Lower Char
Limit in Orig Unit
Reference Range Upper Char
Limit in Orig Unit
Character Result/Finding Char
in Std Format

Variable
Qualifier
Variable
Qualifier
Result
Qualifier

Numeric Result/Finding Num
in Standard Units

Result
Qualifier

Standard Units

Char (UNIT)
839H

Variable
Qualifier

836H7

Exp

SDTM 2.2.3,
SDTMIG 4.1.5.1
83H

Exp

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTM 2.2.3
840H

841H

LBSTNRLO

Reference Range Lower Num
Limit-Std Units

Variable
Qualifier

LBSTNRHI

Reference Range Upper Num
Limit-Std Units
Reference Range for Char Char
Rslt-Std Units
Reference Range
Char *
Indicator

Variable
Qualifier
Variable
Qualifier
Variable
Qualifier

Completion Status

Record
Qualifier

LBSTNRC
LBNRIND

LBSTAT

Char (ND)
194H

Lower end of reference range for continuous measurements for
Exp
LBSTRESC/LBSTRESN in standardized units. Should be populated only
for continuous results.
Upper end of reference range for continuous measurements in standardized Exp
units. Should be populated only for continuous results.
For normal range values that are character in ordinal scale or if categorical Perm
ranges were supplied (e.g., ―-1 to +1‖, ―NEGATIVE TO TRACE‖).
1. Indicates where the value falls with respect to reference range defined Exp
by LBORNRLO and LBORNRHI, LBSTNRLO and LBSTNRHI, or by
LBSTNRC. Examples: NORMAL, ABNORMAL, HIGH, LOW.
2. Sponsors should specify in the study metadata (Comments column in the
define.xml) whether LBNRIND refers to the original or standard reference
ranges and results.
3. Should not be used to indicate clinical significance.
Used to indicate exam not done. Should be null if a result exists in
Perm
LBORRES.

SDTM 2.2.3
SDTM 2.2.3
SDTM 2.2.3
8

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG Appendix
C1
843H

84H

845H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable Name
LBREASND

Variable Label
Reason Test Not Done

Controlled
Type Terms, Codelist
Role
or Format
Char
Record
Qualifier

LBNAM

Vendor Name

Char

LBLOINC

LOINC Code

Char *

CDISC Notes

References

Core

Describes why a measurement or test was not performed such as BROKEN Perm
EQUIPMENT, SUBJECT REFUSED, or SPECIMEN LOST. Used in
conjunction with LBSTAT when value is NOT DONE.
Perm
The name or identifier of the laboratory that performed the test .

Record
Qualifier
Synonym 1. Dictionary-derived LOINC Code for LBTEST.
Qualifier 2. The sponsor is expected to provide the dictionary name and

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3
846H

847H

Perm SDTM 2.2.3,
SDTMIG 4.1.3.2
84H

version used to map the terms utilizing the define.xml external
codelist attributes
LBSPEC

Specimen Type

Char *

LBSPCCND

Specimen Condition

Char *

LBMETHOD

Method of Test or
Examination
Baseline Flag

Char *

LBBLFL

Char (NY)
1942H

Record
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier

Defines the type of specimen used for a measurement. Examples: SERUM,
PLASMA, URINE.
Free or standardized text describing the condition of the specimen e.g.
HEMOLYZED, ICTERIC, LIPEMIC etc.
Method of the test or examination. Examples: EIA (Enzyme
Immunoassay), ELECTROPHORESIS, DIPSTICK
Indicator used to identify a baseline value. The value should be ―Y‖ or
null.

Perm SDTM 2.2.3
Perm SDTM 2.2.3
Perm SDTM 2.2.3
Exp

SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG Appendix
C1
Indicator used to identify fasting status such as Y, N, U, or null if not
Perm SDTM 2.2.3,
relevant.
SDTMIG Appendix
C1
Used to indicate a derived record. The value should be Y or null. Records Perm SDTM 2.2.3,
that represent the average of other records, or do not come from the CRF,
SDTMIG 4.1.3.7,
or are not as originally received or collected are examples of records that
SDTMIG 4.1.5.1,
might be derived for the submission datasets. If LBDRVFL=Y, then
SDTMIG Appendix
LBORRES may be null, with LBSTRESC, and (if numeric) LBSTRESN
C1
having the derived value.
Description of toxicity quantified by LBTOXGR. The sponsor is expected Perm SDTM 2.2.3
to provide the name of the scale and version used to map the terms,
utilizing the define.xml external codelist attributes.
Records toxicity grade value using a standard toxicity scale (such as the
Perm SDTM 2.2.3
NCI CTCAE). If value is from a numeric scale, represent only the number
(e.g., ―2‖ and not ―Grade 2‖).
849H

850H

LBFAST

LBDRVFL

Fasting Status

Derived Flag

Char (NY)
1943H

Char (NY)
194H

Record
Qualifier
Record
Qualifier

851H

852H

853H

854H

LBTOX

Toxicity

Char *

Variable
Qualifier

LBTOXGR

Standard Toxicity Grade Char *

Variable
Qualifier

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Variable Name
VISITNUM

Variable Label
Visit Number

Controlled
Type Terms, Codelist
Role
or Format
Num
Timing

CDISC Notes

References

Core

1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Exp

1. Protocol-defined description of clinical encounter
2. May be used in addition to VISITNUM and/or VISITDY

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.2
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
85H

856H

VISIT

Visit Name

Char

Timing

857H

85H

VISITDY

Planned Study Day of
Visit

Num

Date/Time of Specimen
Collection

Char ISO 8601

End Date/Time of
Specimen Collection

Char ISO 8601

Study Day of Specimen
Collection

Num

Timing

859H

860H

LBDTC

Timing

Exp
861H

862H

LBENDTC

Timing

Perm
863H

864H

LBDY

Timing

1. Study day of specimen collection, measured as integer days.
Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.
1. Text Description of time when specimen should be taken.
Perm SDTM 2.2.5,
2. This may be represented as an elapsed time relative to a fixed reference
SDTMIG 4.1.4.10
point, such as time of last dose. See LBTPTNUM and LBTPTREF.
Examples: Start, 5 min post.
Numerical version of LBTPT to aid in sorting.
Perm SDTM 2.2.5,
SDTMIG 4.1.4.10
Planned Elapsed time (in ISO 8601) relative to a planned fixed reference
Perm SDTM 2.2.5,
(LBTPTREF). This variable is useful where there are repetitive measures. Not
SDTMIG 4.1.4.3,
SDTMIG 4.1.4.10
a clock time or a date time variable. Represented as an ISO 8601 duration.
865H

86H

LBTPT

LBTPTNUM
LBELTM

Planned Time Point
Name

Char

Timing

Planned Time Point
Number
Planned Elapsed Time
from Time Point Ref

Num

Timing

Char ISO 8601

Timing

867H

86H

869H

870H

Examples: ―-PT15M‖ to represent the period of 15 minutes prior to the
reference point indicated by LBTPTREF, or ―PT8H‖ to represent the
period of 8 hours after the reference point indicated by LBTPTREF.
LBTPTREF

Time Point Reference

Char

Timing

LBRFTDTC

Date/Time of Reference
Time Point

Char ISO 8601

Timing

Name of the fixed reference point referred to by LBELTM, LBTPTNUM, Perm SDTM 2.2.5,
and LBTPT. Examples: PREVIOUS DOSE, PREVIOUS MEAL.
SDTMIG 4.1.4.10
Date/time of the reference time point, LBTPTREF.
Perm SDTM 2.2.5,
SDTMIG 4.1.4.10
871H

872H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.3.1 ASSUMPTIONS FOR LABORATORY TEST RESULTS DOMAIN MODEL
1.

LB Definition: This domain captures laboratory data collected on the CRF or received from a central provider or vendor

For lab tests that do not have continuous numeric results (e.g., urine protein as measured by dipstick, descriptive tests such as urine color), LBSTNRC could
be populated either with normal range values that are character in an ordinal scale (e.g., ―NEGATIVE to TRACE') or a delimited set of values that are
considered to be normal (e.g., ―YELLOW‖, ―AMBER‖). LBORNRLO, LBORNRHI, LBSTNRLO, and LBSTNRHI should be null for these types of tests.

LBNRIND can be added to indicate where a result falls with respect to reference range defined by LBORNRLO and LBORNRHI. Examples: HIGH, LOW.
Clinical significance would be represented as described in Section 4.1.5.5 as a record in SUPPLB with a QNAM of LBCLSIG (see also LB Example 1
below).
873H

For lab tests where the specimen is collected over time, i.e., 24-hour urine collection, the start date/time of the collection goes into LBDTC and the end
date/time of collection goes into LBENDTC. See Assumption 4.1.4.8.
874H

The following Qualifiers would not generally be used in LB: --BODSYS, --SEV.

A value derived by a central lab according to their procedures is considered collected rather than derived. See Section 4.1.1.8.1.
875H

6.3.3.2 EXAMPLES FOR LABORATORY TEST RESULTS DOMAIN MODEL
Example 1:
Row 1:
Rows 2-4:

Shows a value collected in one unit, but converted to selected standard unit. See Section 4.1.5.1 for additional examples for the population of Result Qualifiers.
Show two records (rows 2 and 3) for Alkaline Phosphatase done at the same visit, one day apart. Row 4 shows how to create a derived record
(average of the records 2 and 3) and flagged derived (LBDRVFL = ―Y‖) and as the record to use as baseline (LBBLFL = ―Y‖).
Rows 6 and 7: Show a suggested use of the LBSCAT variable. It could be used to further classify types of tests within a laboratory panel (i.e., ―DIFFERENTIAL‖).
Row 9:
Shows the proper use of the LBSTAT variable to indicate "NOT DONE", where a reason was collected when a test was not done.
Row 10:
The subject had cholesterol measured. The normal range for this test is <200 mg/dL. Note that the sponsor has decided to make LBSTNRHI
=199 however another sponsor may have chosen a different value.
Row 12:
Shows use of LBSTNRC for Urine Protein that is not reported as a continuous numeric result.

876H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
lb.xpt
Row STUDYID
ABC
1
ABC
2
ABC
3
ABC
4

DOMAIN
LB
LB
LB
LB

USUBJID
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001

LBSEQ
1
2
3
4

LBTESTCD
ALB
ALP
ALP
ALP

LBTEST
Albumin
Alkaline Phosphatase
Alkaline Phosphatase
Alkaline Phosphatase

ABC

ABC-001-001

WBC

Leukocytes

ABC

ABC-001-001

LYMLE

ABC

ABC-001-001

NEUT

Neutrophils

8
9
10

ABC
ABC
ABC

LB
LB
LB

ABC-001-001
ABC-001-001
ABC-001-001

8
9
10

PH
ALB
CHOL

pH
Albumin
Cholesterol

ABC

ABC-001-001

WBC

Leukocytes

ABC

ABC-001-001

PROT

Protein

Lymphocytes

LBCAT
LBSCAT
CHEMISTRY
CHEMISTRY
CHEMISTRY
CHEMISTRY
HEMATOLO
GY
HEMATOLO
DIFFERENTIAL
GY
HEMATOLO
DIFFERENTIAL
GY
URINALYSIS
CHEMISTRY
CHEMISTRY
HEMATOLO
GY
URINALYSIS

LBORRES
30
398
350

LBORRESU LBORNRLO
g/L
35
IU/L
40
IU/L
40

LBORNRHI
50
160
160

LBSTRESC
3.0
398
350
374

LBSTRESN
3.0
398
350
374

5.9

10^9/L

5.9

6.7

5.1

10^9/L

5.1

7.5

5.1

5.0

9.0

7.5
229

229

5.9

229

mg/dL

<200

5.9

10^9/L

MODERATE

Note that the use of 10^9 as a unit is not a standard representation.
Row
LBSTRESU
g/dL
1 (cont)
units/L
2 (cont)
units/L
3 (cont)
units/L
4 (cont)
10^3/uL
5 (cont)
%
6 (cont)
10^9/L
7 (cont)
8 (cont)
9 (cont)
mg/dL
10 (cont)
11 (cont) 10^3/uL

LBSTNRLO
3.5
40
40
40
4
25
2
5.00

LBSTNRHI
5
160
160
160
11
40
8
9.00

0
4

199
11

LBNRIND
LOW

LB STAT

LBREASND

NOT DONE

INSUFFICIENT SAMPLE

LOW

LBBLFL LBFAST LBDRVFL
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y

Y
NEGATIVE
ABNORMAL
to TRACE

12 (cont)

supplb.xpt
Row 1, 6:

LBSTRNRC

VISITNUM
1
1
1
1
1
1
1
1
2
2
2

VISIT
Week 1
Week 1
Week 1
Week 1
Week 1
Week 1
Week 1
Week 1
Week 2
Week 2
Week 2

LBDTC
1999-06-19
1999-06-19
1999-06-20
1999-06-19
1999-06-19
1999-06-19
1999-06-19
1999-06-19
1999-07-21
1999-07-21
1999-07-21

Week 2

1999-07-21

The SUPPLB dataset example shows clinical significance assigned by the investigator for test results where LBNRIND (reference range indicator) is populated.

Row STUDYID
RDOMAIN
LB
1 ABC
LB
2 ABC

Page 138
November 12, 2008

USUBJID
ABC-001-001
ABC-001-001

IDVAR
LBSEQ
LBSEQ

IDVARVAL
QNAM
1
LBCLSIG
6
LBCLSIG

QLABEL
Clinical Significance
Clinical Significance

QVAL
N
N

QORIG
CRF
CRF

QEVAL
INVESTIGATOR
INVESTIGATOR

CDISC SDTM Implementation Guide (Version 3.1.2)

Example 2
Rows 1:

Shows an example of a pre-dose urine collection interval (from 4 hours prior to dosing until 15 minutes prior to dosing) with a negative value for
LBELTM that reflects the end of the interval in reference to the fixed reference LBTPTREF, the date of which is recorded in LBRFTDTC.
Rows 2 and 3: Show an example of post-dose urine collection intervals with values for LBELTM that reflect the end of the intervals in reference to the fixed
reference LBTPTREF, the date of which is recorded in LBRFTDTC.
Row 1
Row 2
Row 3

STUDYID
ABC
ABC
ABC

Row 1 (cont)
Row 2 (cont)
Row 3 (cont)

DOMAIN
LB
LB
LB

LBSTRESC
0.38
0.61
0.5

USUBJID
ABC-001-001
ABC-001-001
ABC-001-001
LBSTRESN
0.38
0.61
0.5

LBDTC
1999-06-19T04:00
1999-06-19T08:00
1999-06-19T16:00

LBSEQ
1
2
3

LBTESTCD
GLUCOSE
GLUCOSE
GLUCOSE

LBSTRESU
mmol/L
mmol/L
mmol/L

LBENDTC
1999-06-19T07:45
1999-06-19T16:00
1999-06-20T00:00

LBTEST
Glucose
Glucose
Glucose

LBSTNRLO
0.1
0.1
0.1

LBCAT
URINALYSIS
URINALYSIS
URINALYSIS

LBORRES
7
11
9

LBSTNRHI
0.8
0.8
0.8

LBTPT
Pre-dose
0-8 hours after dosing
8-16 hours after dosing

LBORRESU
mg/dL
mg/dL
mg/dL

LBNRIND
NORMAL
NORMAL
NORMAL

LBTPTNUM
1
2
3

LBORNRLO
1
1
1

VISIT
INITIAL DOSING
INITIAL DOSING
INITIAL DOSING

LBELTM
-PT15M
PT8H
PT16H

LBTPTREF
Dosing
Dosing
Dosing

LBORNRHI
15
15
15
VISITNUM
2
2
2

LBRFTDTC
1999-06-19T08:00
1999-06-19T08:00
1999-06-19T08:00

Example 3:
This is an example of pregnancy test records, one with a result and one with no result because the test was not performed due to the subject being male.
Row 1:
Row 2:

Shows an example of a pregnancy test record that returns a result of ―-― (negative sign) in LBORRES and is standardized to the text value
―NEGATIVE‖ in LBSTRESC
Show an example of a pregnancy test that was not performed because the subject was male, and the sponsor felt it was necessary to report a record
documenting the reason why the test was not performed, rather then simply excluding the record.

Row
1
2

STUDYID
ABC
ABC

Row
1 (cont)
2 (cont)

LBORNRLO

Row
LBSTAT
1 (cont)
2 (cont) NOT DONE

DOMAIN
LB
LB
LBORNRHI

USUBJID
ABC-001-001
ABC-001-002
LBSTRESC
NEGATIVE

LBSEQ
1
1

LBTESTCD
HCG
HCG

LBSTRESN

LBREASND
NOT APPLICABLE (SUBJECT MALE)

VISIT
BASELINE
BASELINE

LBTEST
Choriogonadotropin Beta
Choriogonadotropin Beta
LBSTRESU

VISITNUM
1
1

LBSTNRLO

LBCAT
CHEMISTRY
CHEMISTRY

LBORRES
-

LBSTRNHI

LBORRESU

LBNRIND

LBDTC
1999-06-19T04:00
1999-06-24T08:00

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.4 PHYSICAL EXAMINATION — PE
pe.xpt, Physical Examination — Findings, Version 3.1.2. One record per body system or abnormality per visit per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char PE
Identifier Two-character abbreviation for the domain.
1945H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
87H

87H

USUBJID

Unique Subject Identifier

Char

PESEQ

Sequence Number

Num

PEGRPID

Group ID

Char

PESPID

Sponsor-Defined Identifier Char

PETESTCD

PETEST

Body System Examined
Short Name

Body System Examined

Char *

Identifier Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain for a
subject.
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an
explicit line identifier or defined in the sponsor‘s operational database.
Example: Line number on a CRF.
Topic
Short name of the measurement, test, or examination described in
PETEST. It can be used as a column name when converting a dataset
from a vertical to a horizontal format. The value in PETESTCD cannot be
longer than 8 characters, nor can it start with a number (e.g.‖1TEST‖).
PETESTCD cannot contain characters other than letters, numbers, or
underscores.
Synonym Verbatim term part of the body examined. The value in PETEST cannot
Qualifier be longer than 40 characters. Examples: Cardiovascular and Respiratory.
For subject-level exam, value should be ―Physical Examination‖.

Req
879H0

Req
Perm

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
81H

Perm
82H

Req

SDTM 2.2.3,
SDTMIG 4.1.1.9,
SDTMIG 4.1.2.1
83H

Req

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
SDTM 2.2.3,
SDTMIG 4.1.3.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.3.5
86H

87H

PEMODIFY Modified Reported Term
PECAT

Char

Category for Examination Char *

PESCAT

Subcategory for
Examination
PEBODSYS Body System or Organ
Class

Page 140
November 12, 2008

Char *
Char

Synonym
Qualifier
Grouping
Qualifier
Grouping
Qualifier
Result
Qualifier

If PEORRES is modified as part of a defined procedure, then PEMODIFY Perm
will contain the modified text.
Used to define a category of examination. Examples: GENERAL,
Perm
NEUROLOGICAL.
A further categorization of the examination. Used if needed to add further Perm
detail to PECAT.
1. Body system or organ class ( MedDRA SOC) that is involved in a
Perm
measurement from the standard hierarchy (e.g., MedDRA).
89H

890H

891H

892H

CDISC SDTM Implementation Guide (Version 3.1.2)
Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
PEORRES
Verbatim Examination
Char
Result
Text description of any abnormal findings. If the examination was
Finding
Qualifier completed and there were no abnormal findings, the value should be
NORMAL. If the examination was not performed on a particular body
system, or at the subject level, then the value should be null, and NOT
DONE should appear in PESTAT.
PEORRESU Original Units
Char (UNIT)
Variable Original units in which the data were collected. The unit for PEORRES.
Qualifier
PESTRESC Character Result/Finding in Char
Result
If there are findings for a body system, then either the dictionary preferred
Std Format
Qualifier term (if findings are coded using a dictionary) or PEORRES (if findings
are not coded) should appear here. If PEORRES is null, PESTRESC
should be null
PESTAT
Completion Status
Char (ND)
Record
Used to indicate exam not done. Should be null if a result exists in
Qualifier PEORRES.
Variable
Name

Variable Label

894H

1946H

Core

References

Exp

SDTM 2.2.3,
SDTMIG 4.1.3.6
893H

Perm

SDTM 2.2.3,
SDTMIG 4.1.3.2
SDTM 2.2.3,
SDTMIG 4.1.3.6,
SDTMIG 4.1.5.1
895H

Exp
896H

897H

Perm

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3
SDTMIG 4.1.1.9
SDTM 2.2.3
89H

89H

90H

PEREASND Reason Not Examined

Char

PELOC

Location of Physical Exam Char (LOC)
Finding
PEMETHOD Method of Test or
Char *
Examination
PEEVAL
Evaluator
Char *

VISITNUM

903H

Visit Number

Num

Record
Describes why an examination was not performed or why a body system
Qualifier was not examined. Example: SUBJECT REFUSED. Used in conjunction
with STAT when value is NOT DONE.
Record
Can be used to specify where a physical exam finding occurred. Example:
Qualifier LEFT ARM for skin rash.
Record
Method of the test or examination. Examples: XRAY, MRI.
Qualifier
Record
Role of the person who provided the evaluation. Used only for results that
Qualifier are subjective (e.g., assigned by a person or a group). Should be null for
records that contain collected or derived data. Examples:
INVESTIGATOR, ADJUDICATION COMMITTEE, VENDOR.
Timing
1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Perm

Timing

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

901H

902H

Perm
Perm
Perm

SDTM 2.2.3,
SDTMIG 4.1.5.4
904H

Exp

SDTM 2.2.5
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.8
905H

906H

VISIT

Visit Name

Char

907H

908H

VISITDY

Planned Study Day of Visit Num

Timing

90H

910H

PEDTC

Date/Time of Examination Char ISO 8601

Timing

Exp
91H

912H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
PEDY

Controlled
Type Terms, Codelist
Role
or Format
Study Day of Examination Num
Timing
Variable Label

CDISC Notes
1. Study day of physical exam, measured as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.

Core
Perm

References
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
913H

914H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.4.1 ASSUMPTIONS FOR PHYSICAL EXAMINATION DOMAIN MODEL
1.

PE Definition: Data that captures findings about physical exams. This could be information about which body systems that were examined and specific
abnormalities were collected.

The PE domain provides an example where the result, PEORRES, is coded. This is in contrast to Events and Interventions domains (e.g., AE, CM, and MH),
in which the topic variable (AETERM, CMTRT, and EXTRT, respectively) is the one coded.

The following Qualifiers would not generally be used in PE: --XFN, --NAM, --LOINC, --FAST, --TOX, --TOXGR.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.4.2 EXAMPLES FOR PHYSICAL EXAMINATION DOMAIN MODEL
The example shows data for one subject collected at two different visits. In all of the records except 8 and 13 the data comes from the general physical
examination. In this case PECAT is "GENERAL". Additional data collected about an ophthalmologic examination is also added to this domain.
Row 1:
Shows how PESTRESC is populated if result is ―NORMAL‖.
Row 2:
Shows the proper use of the --STAT variable to indicate "NOT DONE", and when PEREASND is used to indicate why a body system
(PETEST) was not examined.
Rows 4-6:
Show how PESPID is used to show the sponsor-defined identifier, which in this case is a CRF sequence number used for identifying
abnormalities within a body system.
Rows 4-7:
Show how PESTRESC is populated if abnormality is dictionary coded.
Rows 8, 13: Show how the PECAT variable can be used to indicate a different type of physical examination. In this case, the ophthalmologic examination
may have been collected in a separate dataset in the operational database.
Row
1
2
3
4
5
6
7
8
9
10
11
12
13

STUDYID DOMAIN
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE
ABC
PE

USUBJID
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001

PESEQ PESPID PETESTCD
1
1
HEAD
2
1
RESP
3
1
ENT
4
1
SKIN
5
2
SKIN
6
3
SKIN
7
1
CV
8
1
FUNDOSCP
9
1
RESP
10
1
ENT
11
1
NECK
12
1
CARDIO
13
1
FUNDOSCP

PETEST
Head
Respiratory
Ear/nose/throat
Skin
Skin
Skin
Cardiovascular
Fundoscopic
Respiratory
Ear/nose/throat
Neck
Cardiovascular
Fundoscopic

PECAT
GENERAL
GENERAL
GENERAL
GENERAL
GENERAL
GENERAL
GENERAL
OPHTHAMOLOGIC
GENERAL
GENERAL
GENERAL
GENERAL
OPHTHAMOLOGIC

Row
PEORRES
PESTRESC
PESTAT
PEREASND
VISITNUM
NORMAL
NORMAL
1
1 (cont)
NOT DONE INVESTIGATOR ERROR
1
2 (cont)
NORMAL
NORMAL
1
3 (cont)
ACNE
ACNE NOS
1
4 (cont)
DERMATITIS
1
5 (cont) ALLERGIC REACTION
SKINRASH
RASH
1
6 (cont)
HEART MURMUR
CARDIAC MURMUR
1
7 (cont)
NORMAL
NORMAL
1
8 (cont)
NORMAL
NORMAL
2
9 (cont)
NORMAL
NORMAL
2
10 (cont)
NORMAL
NORMAL
2
11 (cont)
NORMAL
NORMAL
2
12 (cont)
NORMAL
NORMAL
2
13 (cont)

VISIT
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
VISIT 1
VISIT 1
VISIT 1
VISIT 1
VISIT 1

PELOC

PEBODSYS

FACE
HANDS
LEFT ARM

SKIN
SKIN
SKIN
CARDIOVASCULAR

VISITDY
1
1
1
1
1
1
1
1
45
45
45
45
45

PEDTC
1999-06-06
1999-06-06
1999-06-06
1999-06-06
1999-06-06
1999-06-06
1999-06-06
1999-06-06
1999-07-21
1999-07-21
1999-07-21
1999-07-21
1999-07-21

PEDY
-3
-3
-3
-3
-3
-3
-3
-3
45
45
45
45
45

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.5 QUESTIONNAIRE — QS
qs.xpt, Questionnaires — Findings, Version 3.1.2. One record per questionnaire per question per time point per visit per subject, Tabulation
Variable Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char QS
Identifier Two-character abbreviation for the domain.
1947H

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
915H

916H

USUBJID

Unique Subject Identifier Char

QSSEQ

Sequence Number

Num

QSGRPID

Group ID

Char

QSSPID

Sponsor-Defined
Identifier

Char

Question Short Name

Char *

QSTESTCD

QSTEST

Question Name

Char

Identifier Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain for a
subject.
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an
explicit line identifier or defined in the sponsor‘s operational database.
Example: Question number on a questionnaire.
Topic
Topic variable for QS. Short name for the value in QSTEST, which can be
used as a column name when converting the dataset from a vertical format
to a horizontal format. The value in QSTESTCD cannot be longer than 8
characters, nor can it start with a number (e.g.‖1TEST‖). QSTESTCD
cannot contain characters other than letters, numbers, or underscores.
Examples: COG01, GH1, PF1.
Synonym Verbatim name of the question or group of questions used to obtain the
Qualifier measurement or finding. The value in QSTEST cannot be longer than 40
characters. Example: In General, How is Your Health?

Req
917H8

Req

Perm SDTM 2.2.4,
SDTMIG 4.1.2.6
Perm SDTM 2.2.4,
SDTMIG 4.1.2.6
91H

920H

Req

SDTM 2.2.3,
SDTMIG 4.1.1.9
SDTMIG 4.1.2.1
921H3

Req

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
924H

925H

926H

QSCAT

Category of Question

QSSCAT

Subcategory for Question Char *

QSORRES

Char *

Finding in Original Units Char

Page 144
November 12, 2008

Grouping
Qualifier
Grouping
Qualifier
Result
Qualifier

Used to define a category of related records that will be meaningful to the Req
Reviewer. Examples: HAMILTON DEPRESSION SCALE, SF36, ADAS.
A further categorization of the questions within the category. Examples:
Perm
MENTAL HEALTH DOMAIN, DEPRESSION DOMAIN, WORD
RECALL.
Finding as originally received or collected (e.g. RARELY, SOMETIMES). Exp
When sponsors apply codelist to indicate the code values are statistically
meaningful standardized scores, which are defined by sponsors or by
valid methodologies such as SF36 questionnaires, QSORRES will
contain the decode format, and QSSTRESC and QSSTRESN may contain
the standardized code values or scores.

SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
927H

928H

SDTM 2.2.3,
SDTMIG 4.1.5.1
92H30

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable Name
QSORRESU

QSSTRESC

QSSTRESN

QSSTRESU

Variable Label
Original Units

Controlled
Type Terms, Codelist
Role
or Format
Char (UNIT)
Variable
Qualifier
931H

Character Result/Finding Char
in Std Format

Result
Qualifier

Numeric Finding in
Standard Units

Num

Result
Qualifier

Standard Units

Char (UNIT)
937H

Variable
Qualifier

CDISC Notes
Original units in which the data were collected. The unit for QSORRES,
such as minutes or seconds or the units associated with a visual analog
scale.
Contains the finding for all questions or sub-scores, copied or derived from
QSORRES in a standard format or standard units. QSSTRESC should store
all findings in character format; if findings are numeric, they should also be
stored in numeric format in QSSTRESN. If question scores are derived from
the original finding, then the standard format is the score. Examples: 0, 1.
When sponsors apply codelist to indicate the code values are statistically
meaningful standardized scores, which are defined by sponsors or by valid
methodologies such as SF36 questionnaires, QSORRES will contain the
decode format, and QSSTRESC and QSSTRESN may contain the
standardized code values or scores.
Used for continuous or numeric findings in standard format; copied in
numeric format from QSSTRESC. QSSTRESN should store all numeric
results or findings.
Standardized unit used for QSSTRESC or QSSTRESN.

Core

References

Perm SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
Exp SDTM 2.2.3,
SDTMIG 4.1.5.1
932H

93H

934H5

Perm SDTM 2.2.3,
SDTMIG 4.1.5.1
936H

Perm SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
Used to indicate a questionnaire or response to a questionnaire was not
Perm SDTM 2.2.3,
done. Should be null if a result exists in QSORRES.
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
Describes why a question was not answered. Used in conjunction with
Perm SDTM 2.2.3,
QSSTAT when value is NOT DONE. Example: SUBJECT REFUSED.
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
Indicator used to identify a baseline value. The value should be ―Y‖ or null. Exp SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG
Appendix C1
Used to indicate a derived record. The value should be Y or null. Records that Perm SDTM 2.2.3,
represent the average of other records or questionnaire sub-scores that do not
SDTMIG 4.1.3.7,
come from the CRF are examples of records that would be derived for the
SDTMIG 4.1.5.1,
submission datasets. If QSDRVFL=Y, then QSORRES may be null with
SDTMIG
QSSTRESC and (if numeric) QSSTRESN having the derived value.
Appendix C1
1. Clinical encounter number.
Exp SDTM 2.2.5,
2. Numeric version of VISIT, used for sorting.
SDTMIG 4.1.4.5,
SDTMIG 7.4
938H

93H

QSSTAT

Completion Status

Char (ND)
1948H

Record
Qualifier

940H

941H

942H

QSREASND

Reason Not Performed

Char

Record
Qualifier

943H

94H

QSBLFL

Baseline Flag

Char (NY)
194H

Record
Qualifier

945H

946H

QSDRVFL

Derived Flag

Char (NY)
1950H

Record
Qualifier

947H

948H

94H

VISITNUM

Visit Number

Num

Timing

950H

951H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable Name
VISIT

Variable Label
Visit Name

Controlled
Type Terms, Codelist
Role
or Format
Char
Timing

CDISC Notes
1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Core

References

Perm SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
Perm SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
Exp SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.2
SDTMIG 4.1.4.8
Perm SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
Perm SDTM 2.2.5,
SDTMIG 4.1.4.10
952H

953H

VISITDY

Planned Study Day of
Visit

Num

Date/Time of Finding

Char ISO 8601

Timing

Planned study day of the visit based upon RFSTDTC in Demographics.

954H

95H

QSDTC

Timing

Date of questionnaire.

956H

957H

QSDY

QSTPT

QSTPTNUM
QSELTM

Study Day of Finding

Num

Timing

Planned Time Point
Name

Char

Timing

Planned Time Point
Number
Planned Elapsed Time
from Time Point Ref

Num

Timing

Char ISO 8601

Timing

1. Study day of finding collection, measured as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.
1. Text Description of time when questionnaire should be administered.
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See QSTPTNUM and QSTPTREF.
Numerical version of QSTPT to aid in sorting.

958H

95H

960H

Perm SDTM 2.2.5,
SDTMIG 4.1.4.10
Perm SDTM 2.2.5,
SDTMIG 4.1.4.3,
SDTMIG 4.1.4.10
961H

Planned Elapsed time (in ISO 8601) relative to a planned fixed reference
(QSTPTREF). This variable is useful where there are repetitive measures.
Not a clock time or a date time variable. Represented as an ISO 8601
duration. Examples: ―-PT15M‖ to represent the period of 15 minutes

962H

963H

prior to the reference point indicated by QSTPTREF, or ―PT8H‖ to
represent the period of 8 hours after the reference point indicated by
QSTPTREF.
QSTPTREF

Time Point Reference

Char

Timing

QSRFTDTC

Date/Time of Reference
Time Point
Evaluation Interval

Char ISO 8601

Timing

Char ISO 8601

Timing

QSEVLINT

Name of the fixed reference point referred to by QSELTM, QSTPTNUM,
and QSTPT. Examples: PREVIOUS DOSE, PREVIOUS MEAL.
Date/time of the reference time point, LBTPTREF.

Perm SDTM 2.2.5,
SDTMIG 4.1.4.10
Perm SDTM 2.2.5,
SDTMIG 4.1.4.10
Evaluation Interval associated with a QSTEST question represented in ISO Perm SDTM 2.2.5
8601 character format. Example: "-P2Y" to represent an interval of 2 years
in the question "Have you experienced any episodes in the past 2 years?"
964H

965H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.5.1 ASSUMPTIONS FOR QUESTIONNAIRE DOMAIN MODEL
1.

QS Definition: A written or electronic survey instrument comprised of a series of questions, designed to measure a specific item or set of items. Questionnaires
are research instruments that usually have a documented method of administration, a standard format for data collection, and documented methods for scoring,
analysis, and interpretation of results. A questionnaire is often analyzed by applying a numeric scoring system to question responses, where each question
response is assigned a specific numeric ―score‖ that can be totaled to give an overall score (and possibly sectional sub-scores). Questionnaire data may
include, but are not limited to subject reported outcomes and validated or non-validated questionnaires. The QS domain is not intended for use in submitting a
set of questions grouped on the CRF for convenience of data capture. Some diaries are vehicles for collecting data for a validated questionnaire while others
may simply facilitate capture of routine study data. When objective numeric data with result Qualifiers are collected in a questionnaire or diary format, the
sponsor should consider whether this data actually belongs in a separate (new or existing) domain. For example, if the subject records the number of
caffeinated beverages consumed each day in a diary, this information might be more appropriate for the Substance Use domain. The names of the
questionnaires should be described under the variable QSCAT in the questionnaire domain. These could be either abbreviations or longer names, at the
sponsor‘s discretion until controlled terminology is developed. For example, Alzheimer's Disease Assessment Scale (ADAS), SF-36 Health Survey (SF36),
Positive and Negative Syndrome Scale (PANSS).

Names of subcategories for groups of items/questions could be described under QSSCAT.

Derived information such as total scores and sub scores, etc., may be stored in the QS domain as derived records with appropriate category/subcategory names
(QSSCAT), item names (QSTEST), and results (QSSTRESC, QSSTRESN). Derived records should be flagged by QSDRVFL. Single score measurements or
results may go into questionnaire (e.g., APACHE Score, ECOG), but the sponsor should consider if the results should go into a more appropriate domain.

The following Qualifiers would not generally be used in QS: --POS, --BODSYS, --ORNRLO, --ORNRHI, --STNRLO, --STNRHI, --STRNC, --NRIND,
--RESCAT, --XFN, --LOINC, --SPEC, --SPCCND, --LOC, --METHOD, --FAST, --TOX, --TOXGR, --SEV.

The sponsor is expected to provide information about the version used for each validated questionnaire in the metadata (using the Comments column in the
define.xml ). This could be provided as value-level metadata for QSCAT. If more than one version of a questionnaire is used in a study, the version used for
each record should be specified in the Supplemental Qualifiers datasets, as described in Section 8.4. The sponsor is expected to provide information about the
scoring rules in the metadata.
96H

If the verbatim question text is > 40 characters, put meaningful text in QSTEST and describe the full text in the study metadata. See section 4.1.5.3.1 for
further information.

967H

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.5.2 EXAMPLES FOR QUESTIONNAIRE DOMAIN MODEL
Example 1:
This is an example of data from a questionnaire from one subject at one visit with standard text answers that have an associated score. In this example the subject
answered all of the questions in Rows 1-4 and Rows 7-9. The standard text (e.g., very good) translates to a score of 4.4. The value of 4.4 is populated in both
QSSTRESN and QSSTRESC. Since this is the baseline data there is a flag in all records in QSBLFL. The values in Rows 5, 6, 10, and 11 are derived from previous
records and are flagged with a Y in QSDRVFL. The example shows how the textual answer is handled in the QSORRES variable, while the QSSTRESC and
QSSTRESN contain the standardized score value.
Rows 5, 6, 10, 11

Show derived records. Notice how QSORRES is blank for derived records because there is no corresponding text value for the numeric
value shown (see Section 4.1.5.1).
968H

Row
1
2
3
4
5
6
7
8
9
10
11

STUDYID
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX

Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)

DOMAIN
QS
QS
QS
QS
QS
QS
QS
QS
QS
QS
QS

QSORRES
VERY GOOD
MOSTLY FALSE
MOSTLY TRUE
DEFINITELY FALSE

Page 148
November 12, 2008

NO
NO
NO

USUBJID
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001

QSSEQ
1
2
3
4
5
6
7
8
9
10
11

QSSTRESC
4.4
4
4
5
21.4
82
2
2
2
8
100

QSTESTCD
GH1
GH11A
GH11B
GH11C
GH
GHINDEX
RP4A
RP4B
RP4C
RP
RPINDEX

QSSTRESN
4.4
4
4
5
21.4
82
2
2
2
8
100

QSTEST
Health
Sick a little easier
Healthy as anybody
Expect health to get worse
SF-36 General health perceptions
SF-36 General health perceptions (0-100)
Phys. Health-cut down time spent
Phys. Health-accomplished less
Phys. Health-limit kind of work
SF-36 Role-physical
SF-36 Role-physical (0-100)

QSBLFL
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y

QSDRVFL

Y
Y

VISITNUM
2
2
2
2
2
2
2
2
2
2
2

QSCAT
SF36
SF36
SF36
SF36
SF36
SF36
SF36
SF36
SF36
SF36
SF36

VISIT
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE
BASELINE

QSSCAT
GENERAL HEALTH
GENERAL HEALTH
GENERAL HEALTH
GENERAL HEALTH
GENERAL HEALTH
GENERAL HEALTH
ROLE-PHYSICAL
ROLE-PHYSICAL
ROLE-PHYSICAL
ROLE-PHYSICAL
ROLE-PHYSICAL

QSDTC
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28
2001-03-28

QSDY
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2

CDISC SDTM Implementation Guide (Version 3.1.2)

Example 2:
This example shows data from one subject collected at one visit for a questionnaire with standard text answers.
Rows 1-10: Answers are not associated with a numeric score, so QSSTRESC is copied from QSORRES, and QSSTRESN is null. Notice that QSTPTNUM is
used to distinguish the same question being asked at various time points on the same date where no time was collected. For more information on
time points, see Section 4.1.4.10. Note that QSTPTREF is not used in the examples because QSTPTNUM is being used only to organize the results
by type of question, and the timing to a reference point is not important in this study. In this study, QSTPTNUM is an arbitrary number, sponsor
defined to aid in sorting
Row 11:
Shows a derived record. Notice how QSORRES is blank for derived records because there is no corresponding text value for the numeric value
shown (see Section 4.1.5.1). The derived record, however, does have a derived value in QSSTRESN.
96H

970H

Row
1
2
3
4
5
6
7
8
9
10
11

Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)

STUDYID
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX
STUDYX

DOMAIN
QS
QS
QS
QS
QS
QS
QS
QS
QS
QS
QS

QSSTRESN

QSBLFL

USUBJID
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001
P0001

QSSEQ
1
2
3
4
5
6
7
8
9
10
11

QSTESTCD
COG01T02
COG01T02
COG01T02
COG01T03
COG01T03
COG01T03
COG01T04
COG01T04
COG01T04
COG01T09
COG01X

QSTEST
ARM
ARM
ARM
BUTTER
BUTTER
BUTTER
CABIN
CABIN
CABIN
GRASS
WORD RECALL

QSCAT
ADAS
ADAS
ADAS
ADAS
ADAS
ADAS
ADAS
ADAS
ADAS
ADAS
ADAS

QSSCAT
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL
WORD RECALL

QSORRES
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO

QSDRVFL

VISITNUM

VISIT

VISITYDY

QSDTC

QSDY

QSTPTNUM

SCREENING
SCREENING
SCREENING
SCREENING
SCREENING
SCREENING
SCREENING
SCREENING
SCREENING
SCREENING
SCREENING

-14
-14
-14
-14
-14
-14
-14
-14
-14
-14
-14

2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20
2001-03-20

-10
-10
-10
-10
-10
-10
-10
-10
-10
-10
-10

1
2
3
1
2
3
1
2
3
1

1
1
1
1
1
1
1
1
1
1
1

QSSTRESC
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
9

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.6 SUBJECT CHARACTERISTICS — SC
sc.xpt, Subject Characteristics — Findings, Version 3.1.2. One record per characteristic per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled Terms,
Codelist or Format

Type

Char
Char SC
1952H

Role

CDISC Notes

Identifier Unique identifier for a study.
Identifier Two-character abbreviation for the domain.

Core
Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
971H

972H

USUBJID

Char

SCSEQ

Unique Subject
Identifier
Sequence Number

SCGRPID

Group ID

Char

SCSPID

Sponsor-Defined
Identifier
Subject Characteristic
Short Name

Char

SCTESTCD

SCTEST

Subject Characteristic

Num

Char (SCCD)
97H

Char *

973H4

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.1.9
SDTMIG 4.1.2.1
SDTMIG
Appendix C1
975H

976H

978H

980H

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
Perm SDTM 2.2.3,
SDTMIG 4.1.2.6
Perm SDTM 2.2.3,
SDTMIG 4.1.2.6
Exp
SDTM 2.2.3,
SDTMIG 4.1.5.1
Perm SDTM 2.2.3,
SDTMIG 4.1.3.2
SDTMIG 4.1.5.1
981H

982H

983H

SCCAT

Category for Subject
Characteristic
SCSCAT
Subcategory for Subject
Characteristic
SCORRES
Result or Finding in
Original Units
SCORRESU Original Units

Char *
Char *
Char
Char (UNIT)
98H

Grouping
Qualifier
Grouping
Qualifier
Result
Qualifier
Variable
Qualifier

Used to define a category of related records.

984H

A further categorization of the subject characteristic.

985H

Result of the subject characteristic as originally received or collected.

986H

987H

Original Unit in which the data were collected. The unit for SCORRES.

98H

90H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
SCSTRESC

SCSTRESN

SCSTRESU

Variable Label
Character
Result/Finding in Std
Format

Type

Controlled Terms,
Codelist or Format

Char

Result
Qualifier

Numeric Result/Finding Num
in Standard Units
Standard Units

Role

Result
Qualifier

Char (UNIT)
94H

Variable
Qualifier

CDISC Notes

Core

References

Contains the result value for all findings, copied or derived from
Exp
SCORRES in a standard format or standard units. SCSTRESC should
store all results or findings in character format; if results are numeric, they
should also be stored in numeric format in SCSTRESN. For example, if a
test has results ―NONE‖, ―NEG‖, and ―NEGATIVE‖ in SCORRES and
these results effectively have the same meaning, they could be represented
in standard format in SCSTRESC as ―NEGATIVE‖.
Used for continuous or numeric results or findings in standard format;
Perm
copied in numeric format from SCSTRESC. SCSTRESN should store all
numeric test results or findings.
Standardized unit used for SCSTRESC or SCSTRESN.
Perm

SDTM 2.2.3,
SDTMIG 4.1.5.1
91H

92H

SDTM 2.2.3,
SDTMIG 4.1.5.1
93H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.2
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
95H

96H

SCSTAT

Completion Status

Char (ND)
1953H

Record
Qualifier

Used to indicate that the measurement was not done. Should be null if a
result exists in SCORRES.

Perm
97H

98H

SCREASND Reason Not Performed Char

Record
Qualifier

Describes why the observation has no result. Example: subject refused.
Used in conjunction with SCSTAT when value is NOT DONE.

Perm
10H

10H

SCDTC

Date/Time of Collection Char ISO 8601

Timing

Perm
102H

103H

SCDY

Study Day of
Examination

Num

Timing

1. Study day of collection, measured as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.

Perm
104H

105H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.6.1 ASSUMPTIONS FOR SUBJECT CHARACTERISTICS DOMAIN MODEL
1. SC Definition: Subject Characteristics is for data not collected in other domains that are subject-related. Examples: subject initials, eye color, childbearing
status, etc.
2. The structure for demographic data is fixed and includes date of birth, age, sex, race, ethnicity and country. The structure of subject characteristics is based on
the Findings general observation class and is an extension of the demographics data. Subject Characteristics consists of data that is collected once per
subject (per test). SC contains data that is either not normally expected to change during the trial or whose change is not of interest after the initial collection.
Sponsor should ensure that data considered for submission in SC do not actually belong in another domain.

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CDISC SDTM Implementation Guide (Version 3.1.2)
3. The following Qualifiers would not generally be used in SC: --MODIFY, --POS, --BODSYS, --ORNRLO, --ORNRHI, --STNRLO, --STNRHI, --STNRC,
--NRIND, --RESCAT, --XFN, --NAM, --LOINC, --SPEC, --SPCCND, --METHOD, --BLFL, --FAST, --DRVRL, --TOX, --TOXGR, --SEV.
6.3.6.2 EXAMPLE FOR SUBJECT CHARACTISTICS DOMAIN MODEL
The example below shows data that is collected once per subject and does not fit into the Demographics domain. For this example the eye color and initials were
collected.
Row
1
2
3
4
5
6
7
8

STUDYID

DOMAIN

USUBJID

ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC

SC
SC
SC
SC
SC
SC
SC
SC

ABC-001-001
ABC-001-001
ABC-001-002
ABC-001-002
ABC-001-003
ABC-001-003
ABC-002-001
ABC-002-001

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November 12, 2008

SCSEQ SCTESTCD
1
2
1
2
1
2
1
2

EYECD
SUBJINIT
EYECD
SUBJINIT
EYECD
SUBJINIT
EYECD
SUBJINIT

SCTEST

SCORRES

SCSTRESC

SCDTC

Eye Color
Subject Initials
Eye Color
Subject Initials
Eye Color
Subject Initials
Eye Color
Subject Initials

BROWN
HLT
BLUE
BAM
GREEN
ALM
HAZEL
CQH

1999-06-19
1999-06-19
1999-03-19
1999-03-19
1999-05-03
1999-05-03
1999-06-14
1999-06-14

CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.7 VITAL SIGNS — VS
vs.xpt, Vital Signs — Findings, Version 3.1.2. One record per vital sign measurement per time point per visit per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
or Format
Char
Identifier
Char VS
Identifier
1954H

CDISC Notes
Unique identifier for a study.
Two-character abbreviation for the domain.

Core

Reference

Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
106H

107H

USUBJID

Unique Subject Identifier

Char

VSSEQ

Sequence Number

Num

VSGRPID

Group ID

Char

VSSPID

Sponsor-Defined Identifier Char

VSTESTCD Vital Signs Test Short
Name

VSTEST

Vital Signs Test Name

Identifier

Char (VSTESTCD)
195H

Char (VSTEST)
1956H

Identifier used to uniquely identify a subject across all studies for all
Req
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a Req
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain for a
Perm
subject.
Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm
explicit line identifier or defined in the sponsor‘s operational database.
Topic
Short name of the measurement, test, or examination described in
Req
VSTEST. It can be used as a column name when converting a dataset
from a vertical to a horizontal format. The value in VSTESTCD cannot be
longer than 8 characters, nor can it start with a number (e.g.‖1TEST‖).
VSTESTCD cannot contain characters other than letters, numbers, or
underscores. Examples: SYSBP, DIABP, BMI.
Synonym Verbatim name of the test or examination used to obtain the measurement Req
Qualifier or finding. The value in VSTEST cannot be longer than 40 characters.
Examples: Systolic Blood Pressure, Diastolic Blood Pressure, Body Mass
Index.
108H9

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.1.8,
SDTMIG 4.1.2.1,
SDTMIG
Appendix C1
10H

10H

102H3

104H

105H

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3
106H

107H

108H

109H

VSCAT

Category for Vital Signs

VSSCAT

Subcategory for Vital Signs Char *

VSPOS

Vital Signs Position of
Subject
Result or Finding in
Original Units

VSORRES

Char *

Char (POSITION)
102H

Char

Grouping
Qualifier
Grouping
Qualifier
Record
Qualifier
Result
Qualifier

Used to define a category of related records.

Perm

A further categorization of a measurement or examination.

Perm

Position of the subject during a measurement or examination. Examples:
SUPINE, STANDING, SITTING.
Result of the vital signs measurement as originally received or collected.

Perm

102H

Exp

SDTM 2.2.3,
SDTMIG 4.1.5.1
1023H

1024H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name

Variable Label

VSORRESU Original Units

Controlled
Type Terms, Codelist
Role
or Format
Char (VSRESU)
Variable
Qualifier
1957H

CDISC Notes
Original units in which the data were collected. The unit for VSORRES.
Examples: IN, LB, BEATS/MIN.

Core
Exp

Reference
SDTM 2.2.3,
SDTMIG 4.1.3.2
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1
1025H

1026H

1027H

VSSTRESC

Character Result/Finding in Char
Std Format

Result
Qualifier

VSSTRESN Numeric Result/Finding in Num
Standard Units

Result
Qualifier

VSSTRESU Standard Units

Char (VSRESU)
1958H

Variable
Qualifier

Contains the result value for all findings, copied or derived from
Exp
VSORRES in a standard format or standard units. VSSTRESC should
store all results or findings in character format; if results are numeric, they
should also be stored in numeric format in VSSTRESN. For example, if a
test has results ―NONE‖, ―NEG‖, and ―NEGATIVE‖ in VSORRES and
these results effectively have the same meaning, they could be represented
in standard format in VSSTRESC as ―NEGATIVE‖.
Used for continuous or numeric results or findings in standard format;
Exp
copied in numeric format from VSSTRESC. VSSTRESN should store all
numeric test results or findings.
Standardized unit used for VSSTRESC and VSSTRESN.
Exp
1028H9

SDTM 2.2.3,
SDTMIG 4.1.5.1
103H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3
103H

1032H

103H

VSSTAT

Completion Status

Char (ND)
195H

Record
Qualifier

Used to indicate that a vital sign measurement was not done. Should be
null if a result exists in VSORRES.

Perm
1034H

1035H

1036H

VSREASND Reason Not Performed

VSLOC
VSBLFL

Location of Vital Signs
Measurement
Baseline Flag

Char

Record
Qualifier

Char (LOC)
1039H

Char (NY)
1960H

Record
Qualifier
Record
Qualifier

Describes why a measurement or test was not performed. Examples:
Perm
BROKEN EQUIPMENT or SUBJECT REFUSED. Used in conjunction
with VSSTAT when value is NOT DONE.
Location relevant to the collection of Vital Signs measurement. Example: Perm
LEFT ARM for blood pressure.
Indicator used to identify a baseline value. The value should be ―Y‖ or
Exp
null.
1037H

1038H

SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
104H

104H

VSDRVFL

Derived Flag

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November 12, 2008

Char (NY)
196H

Record
Qualifier

Used to indicate a derived record. The value should be Y or null. Records Perm
which represent the average of other records or which do not come from
the CRF are examples of records that would be derived for the submission
datasets. If VSDRVFL=Y, then VSORRES may be null, with VSSTRESC
and (if numeric) VSSTRESN having the derived value.
1042H

1043H

104H

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
VISITNUM

Variable Label
Visit Number

Controlled
Type Terms, Codelist
Role
or Format
Num
Timing

CDISC Notes

Core

1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Exp

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

Reference
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
SDTM 2.2.5,
SDTMIG 4.1.4.10
1045H

1046H

VISIT

Visit Name

Char

Timing

1047H

1048H

VISITDY

Planned Study Day of Visit Num

Timing

1049H

105H

VSDTC

Date/Time of
Measurements

Char ISO 8601

Study Day of Vital Signs

Num

Timing

Exp
105H

1052H

VSDY

VSTPT

Planned Time Point Name Char

VSTPTNUM Planned Time Point
Number
VSELTM
Planned Elapsed Time
from Time Point Ref

VSTPTREF

Time Point Reference

VSRFTDTC Date/Time of Reference
Time Point

Timing

Num

Timing

Char ISO 8601

Timing

1. Study day of vital signs measurements, measured as integer days.
Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.
1. Text Description of time when measurement should be taken.
Perm
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See VSTPTNUM and VSTPTREF.
Examples: Start, 5 min post.
Numerical version of VSTPT to aid in sorting.
Perm
1053H

1054H

105H

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.3,
SDTMIG 4.1.4.10
1056H

Char

Timing

Char ISO 8601

Timing

Planned Elapsed time (in ISO 8601) relative to a planned fixed reference Perm
(VSTPTREF). This variable is useful where there are repetitive measures.
Not a clock time or a date time variable. Represented as an ISO 8601
Duration. Examples: ―-PT15M‖ to represent the period of 15 minutes
prior to the reference point indicated by VSTPTREF, or ―PT8H‖ to
represent the period of 8 hours after the reference point indicated by
VSTPTREF.
Name of the fixed reference point referred to by VSELTM, VSTPTNUM, Perm
and VSTPT. Examples: PREVIOUS DOSE, PREVIOUS MEAL.
Date/time of the reference time point, LBTPTREF.
Perm
1057H

1058H

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.10
1059H

106H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.7.1 ASSUMPTIONS FOR VITAL SIGNS DOMAIN MODEL
1.

VS Definition: CRF data that captures measurements such as blood pressure, height, weight, pulse, and body temperature, or derived data such as body
mass index.

In cases where the LOINC dictionary is used for Vital Sign tests, the permissible variable VSLOINC could be used. The sponsor is expected to provide
the dictionary name and version used to map the terms utilizing the define.xml external codelist attributes

If a reference range is available for a vital signs test, the variables VSORNRLO,VSORNRHI, VSNRIND from the Findings observation class may be
added to the domain. VSORNRLO and VSORNRHI would represent the reference range , and VSNRIND would be used to indicate where a result falls
in respect to the reference range (examples: HIGH, LOW). Clinical significance would be represented as described in Section 4.1.5.5 as a record in
SUPPVS with a QNAM of VSCLSIG.
106H

The following Qualifiers would not generally be used in VS: --BODSYS, --XFN, --SPEC, --SPCCND, --FAST, --TOX, --TOXGR.
6.3.7.2 EXAMPLE FOR VITAL SIGNS DOMAIN MODEL

The example below shows one subject with two visits, Baseline and Visit 1, including examples of both collected and derived baseline measurements.
Rows 1,2, 4,5, 8, 9: VSTPT and VSTPTNUM are populated since more than one measurement was taken at this visit.
Rows 3, 6:
Show an example of a derived value that was not considered to be an original result. In this case the sponsor derived the value in a different
variable in the operational database. VSTPT and VSTPTNUM are not populated for these derived records.
Rows 8, 9:
Show two temperatures taken at the baseline visit. Row 9 has a "Y" in the VSBLFL to indicate it was used as the baseline measurement.
Row 14:
Shows a value collected in one unit, but converted to selected standard unit.
Row 15:
Shows the proper use of the --STAT variable to indicate "NOT DONE" where a reason was collected when a test was not done.
Row STUDYID DOMAIN USUBJID VSSEQ VSTESTCD
VSTEST
VSPOS VSORRES VSORRESU VSSTRESC VSSTRESN VSSTRESU
ABC
VS
ABC-001-001
1
SYSBP Systolic Blood Pressure SITTING
154
mmHg
154
154
mmHg
1
ABC
VS
ABC-001-001
2
SYSBP Systolic Blood Pressure SITTING
152
mmHg
152
152
mmHg
2
ABC
VS
ABC-001-001
3
SYSBP Systolic Blood Pressure SITTING
153
153
mmHg
3
ABC
VS
ABC-001-001
4
DIABP Diastolic Blood Pressure SITTING
44
mmHg
44
44
mmHg
4
ABC
VS
ABC-001-001
5
DIABP Diastolic Blood Pressure SITTING
48
mmHg
48
48
mmHg
5
ABC
VS
ABC-001-001
6
DIABP Diastolic Blood Pressure SITTING
46
46
mmHg
6
ABC
VS
ABC-001-001
7
PULSE Pulse Rate
SITTING
72
bpm
72
72
bpm
7
ABC
VS
ABC-001-001
8
TEMP
Temperature
34.7
C
34.7
34.7
C
8
ABC
VS
ABC-001-001
9
TEMP
Temperature
36.2
C
36.2
36.2
C
9
ABC
VS
ABC-001-001
10
WEIGHT Weight
STANDING
90.5
kg
90.5
90.5
kg
10
ABC
VS
ABC-001-001
11
HEIGHT Height
STANDING
157
cm
157
157
cm
11
ABC
VS
ABC-001-001
12
SYSBP Systolic Blood Pressure SITTING
95
mmHg
95
95
mmHg
12
ABC
VS
ABC-001-001
13
DIABP Diastolic Blood Pressure SITTING
44
mmHg
44
44
mmHg
13
ABC
VS
ABC-001-001
14
TEMP
Temperature
97.16
F
36.2
36.2
C
14
ABC
VS
ABC-001-001
15
WEIGHT Weight
15

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CDISC SDTM Implementation Guide (Version 3.1.2)
Row

VSSTAT

VSREASND

VSBLFL VSDRVFL

LEFT ARM

1 (cont)

LEFT ARM

2 (cont)
3 (cont)

LEFT ARM

4 (cont)

LEFT ARM

5 (cont)
6 (cont)
7 (cont)

LEFT ARM
LEFT ARM

Y
Y

MOUTH

8 (cont)
9 (cont)
10 (cont)
11 (cont)
12 (cont)
13 (cont)
14 (cont)
15 (cont)

VSLOC

MOUTH

VISITNUM

VISITDY

BASELINE

BASELINE
BASELINE

1
1

BASELINE

BASELINE
BASELINE
BASELINE

1
1
1

BASELINE

BASELINE
BASELINE
BASELINE
VISIT 2
VISIT 2
VISIT 2
VISIT 2

1
1
1
2
2
2
2

1
1
1
35
35
35
35

Y
Y
Y

LEFT ARM
LEFT ARM
MOUTH
NOT DONE

VISIT

Subject refused

VSDTC
1999-0619T08:45
1999-0619T09:00
1999-06-19
1999-0619T08:45
1999-0619T09:00
1999-06-19
1999-06-19
1999-0619T08:45
1999-0619T09:00
1999-06-19
1999-06-19
1999-07-21
1999-07-21
1999-07-21
1999-07-21

VSDY
1

VSTPT
VSTPTNUM
BASELINE 1
1

BASELINE 2

1
1

BASELINE 1

BASELINE 2

1
1
1

BASELINE 1

BASELINE 2

1
1
33
33
33
33

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.8 DRUG ACCOUNTABILITY — DA
da.xpt, Drug Accountability — Findings, Version 3.1.2. One record per drug accountability finding per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study within the submission.
Char DA
Identifier Two-character abbreviation for the domain.
1962H

Core
Req
Req

Reference
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
1062H

1063H

USUBJID

Unique Subject Identifier Char

Identifier Unique subject identifier within the submission.

Req

DASEQ

Sequence Number

Num

DAGRPID

Group ID

Char

DAREFID

Reference ID

Char

Identifier Sequence Number given to ensure uniqueness of subject records within a Req
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain for a
Perm
subject.
Identifier Internal or external identifier such as label number.
Perm

DASPID

Sponsor-Defined
Identifier

Char

Short Name of
Accountability
Assessment

Char *

Name of Accountability
Assessment

Char *

1064H5

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
106H

1067H

DATESTCD

DATEST

DACAT

Category of Assessment Char *

DASCAT

Subcategory of
Assessment
Assessment Result in
Original Units
Original Units

DAORRES
DAORRESU

Char *
Char
Char (UNIT)
1079H

Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an
explicit line identifier or defined in the sponsor‘s operational database.
Examples: Line number on the Drug Accountability page, drug label code.
Topic
Short character value for DATEST used as a column name when
converting a dataset from a vertical format to a horizontal format. The
short value can be up to 8 characters and cannot begin with a number or
contain characters other than letters, numbers or underscores. Example:
DISPAMT, RETAMT.
Synonym Verbatim name, corresponding to the topic variable, of the test or
Qualifier examination used to obtain the drug accountability assessment. The value
in DATEST cannot be longer than 40 characters. Example: Dispensed
Amount, Returned Amount.
Grouping Used to define a category of related records. Examples: STUDY
Qualifier MEDICATION, RESCUE MEDICATION.
Grouping Used to define a further categorization level for a group of related
Qualifier records.
Result
Result of the Drug Accountability assessment as originally received or
Qualifier collected.
Variable Unit for DAORRES.
Qualifier

Perm
1068H

Req

SDTM 2.2.3,
SDTMIG 4.1.1.8
SDTMIG 4.1.2.1
1069H7

107H

Req

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.5.1
SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTMIG
Appendix C1
1072H

1073H

1074H

Perm
1075H

Perm
1076H

Exp
107H8

Perm
108H

108H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
DASTRESC

DASTRESN

DASTRESU

Controlled
Type Terms, Codelist
Role
or Format
Assessment Result in Std Char
Result
Format
Qualifier
Variable Label

Numeric Result/Finding Num
in Standard Units
Assessment Standard
Units

Result
Qualifier

Char (UNIT)
1085H

Variable
Qualifier

CDISC Notes

Core

Reference

Contains the result value for all Drug Accountability assessments, copied or Exp
derived from DAORRES in a standard format or in standard units.
DASTRESC should store all results or findings in character format; if results
are numeric, they should also be stored in numeric format in DASTRESN.
Used for continuous or numeric results or findings in standard format;
Perm
copied in numeric format from DASTRESC. DASTRESN should store
all numeric test results or findings.
Standardized units used for DASTRESC and DASTRESN.
Perm

SDTM 2.2.3,
SDTMIG 4.1.5.1
1082H3

SDTM 2.2.3,
SDTMIG 4.1.5.1
1084H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
1086H

1087H

DASTAT

Completion Status

Char (ND)
1963H

Record
Qualifier

Used to indicate that a drug accountability assessment was not done.
Should be null or have a value of NOT DONE.

Perm
108H

1089H

109H

DAREASND

Reason Not Performed

Char

Record
Qualifier

Reason not done. Used in conjunction with DASTAT when value is NOT Perm
DONE.

Timing

1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Exp

1. Protocol-defined description of clinical encounter
2. May be used in addition to VISITNUM and/or VISITDY

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

109H

1092H

VISITNUM

Visit Number

Num

1094H

1095H

VISIT

Visit Name

Char

Timing

1096H

1097H

VISITDY

Planned Study Day of
Visit

Num

Date/Time of
Accountability
Assessment
Study Day of
Accountability
Assessment

Char ISO 8601

Timing

1098H

109H

DADTC

DADY

Timing

Exp
10H

10H

Num

Timing

1. Study day of drug accountability assessment, measured as integer days. Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.
102H

103H

*indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.8.1 ASSUMPTIONS FOR DRUG ACCOUNTABILITY DOMAIN MODEL
1.

Definition: Drug Accountability is for data regarding the accountability of study drug, such as information on receipt, dispensing, return, and packaging.

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
2.
3.
4.

One way a sponsor may choose to distinguish between different types of medications (e.g., study medication, rescue medication, run-in medication) is to use DACAT.
DAREFID and DASPID are both available for capturing label information.
The following Qualifiers would not generally be used in DA: --MODIFY, --POS, --BODSYS, --ORNRLO, --ORNRHI, --STNRLO, --STNRHI, --STNRC,
--NRIND, --RESCAT, --XFN, --NAM, --LOINC, --SPEC, --SPCCND, --METHOD, --BLFL, --FAST, --DRVRL, --TOX, --TOXGR, --SEV.
6.3.8.2 EXAMPLES FOR DRUG ACCOUNTABILITY DOMAIN MODEL

Example 1
Example 1 below shows drug accounting for a study with two study meds and one rescue med, all of which are measured in tablets. The sponsor has chosen to
add EPOCH from the list of timing variables and to use DASPID and DAREFID for code numbers that appear on the label.
Row STUDYID DOMAIN
1
2
3
4
5
6

ABC
ABC
ABC
ABC
ABC
ABC

Row DAORRES
1 (cont)
30
2 (cont)
5
3 (cont)
15
4 (cont)
0
5 (cont)
10
6 (cont)
10

DA
DA
DA
DA
DA
DA

USUBJID

DASEQ

ABC/01001
ABC/01001
ABC/01001
ABC/01001
ABC/01001
ABC/01001

1
2
3
4
5
6

DAORRESU DASTRESC
TABLETS
30
TABLETS
5
TABLETS
15
TABLETS
0
TABLETS
10
TABLETS
10

DAREFID

DASPID

DATESTCD

XBYCC-E990A
XBYCC-E990A
XBYCC-E990B
XBYCC-E990B

A375827
A375827
A227588
A227588

DISPAMT
RETAMT
DISPAMT
RETAMT
DISPAMT
RETAMT

DASTRESN
30
5
15
0
10
10

DASTRESU
TABLETS
TABLETS
TABLETS
TABLETS
TABLETS
TABLETS

VISITNUM
1
2
1
2
1
2

DATEST
Dispensed Amount
Returned Amount
Dispensed Amount
Returned Amount
Dispensed Amount
Returned Amount

DADTC
2004-06-15
2004-07-15
2004-06-15
2004-07-15
2004-06-15
2004-07-15

DACAT
Study Medication
Study Medication
Study Medication
Study Medication
Rescue Medication
Rescue Medication

DASCAT
Bottle A
Bottle A
Bottle B
Bottle B

EPOCH
Study Med Period 1
Study Med Period 1
Study Med Period 1
Study Med Period 1
Study Med Period 1
Study Med Period 1

Example 2
Example 2 is for a study where drug containers, rather than their contents, are being accounted for and the sponsor did not track returns. In this case, the purpose
of the accountability tracking is to verify that the containers dispensed were consistent with the randomization. The sponsor has chosen to use DASPID to record
the identifying number of the container dispensed .
Row
1
2

STUDYID
ABC
ABC

Row DAORRES
1 (cont)
1
2 (cont)
1

Page 160
November 12, 2008

DOMAIN USUBJID
DA
ABC/01001
DA
ABC/01001

DASEQ
1
2

DAORRESU DASTRESC
CONTAINER
1
CONTAINER
1

DASPID
AB001
AB002

DATESTCD
DISPAMT
DISPAMT

DASTRESN DASTRESU
1
CONTAINER
1
CONTAINER

DATEST
Dispensed Amount
Dispensed Amount

DACAT
Study Medication
Study Medication

DASCAT
Drug A
Drug B

VISITNUM
DADTC
1
2004-06-15
1
2004-06-15
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Final

CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.9 MICROBIOLOGY DOMAINS — MB AND MS
104H

6.3.9.1 MICROBIOLOGY SPECIMEN (MB) DOMAIN MODEL
mb.xpt, Microbiology Specimen — Findings, Version 3.1.2. One record per microbiology specimen finding per time point per visit per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char MB
Identifier Two-character abbreviation for the domain.
1964H

Core

References

Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
105H

106H

USUBJID

Unique Subject Identifier

Char

MBSEQ

Sequence Number

Num

MBGRPID

Group ID

Char

MBREFID

Reference ID

Char

MBSPID

Sponsor-Defined Identifier Char

Identifier

Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain to
support relationships within the domain and between domains. In MB,
used to link to findings about organisms which are stored in MS.
Identifier Internal or external specimen identifier. Example: Specimen ID

Req

Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an
explicit line identifier or defined in the sponsor's operational database.
Example: ORGANISM IDENTIFIER. For organism identification, MBSPID
would remain the same each time the same organism is identified in a new
specimen.
Topic
Short name of the measurement, test, or finding described in MBTEST. It can
be used as a column name when converting a dataset from a vertical to a
horizontal format. The value in MBTESTCD cannot be longer than 8
characters, nor can it start with a number (e.g., ―1TEST‖). MBTESTCD
cannot contain characters other than letters, numbers, or underscores.
Examples for GRAM STAIN findings: GMNROD, GMNCOC,
GMSQEPCE, GMPMNLOW. Examples for CULTURE PLATE findings:
ORGANISM.
Synonym Verbatim name of the test or examination used to obtain the measurement
Qualifier or finding. The value in MBTEST cannot be longer than 40 characters.
Examples: GRAM NEGATIVE RODS, GRAM NEGATIVE COCCI,
SQUAMOUS EPITHELIAL CELLS, PMN PER FIELD LOW,
ORGANISM PRESENT
Grouping Used to define a category of related records.
Qualifier

Perm

107H8

Req
Exp

SDTM 2.2.4,
SDTMIG 4.1.2.6
109H

Perm

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
10H

MBTESTCD Microbiology Test or
Finding Short Name

MBTEST

MBCAT

Microbiology Test or
Finding Name

Char *

Category for Microbiology Char *
Finding

Req

SDTM 2.2.3,
SDTMIG 4.1.1.8,
SDTMIG 4.1.2.1
12H3

14H

Req

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
15H

16H

17H

Perm

SDTM 2.2.3,
SDTMIG 4.1.2.6
18H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
MBSCAT
MBORRES

Variable Label
Subcategory for
Microbiology Finding
Result or Finding in
Original Units

MBORRESU Original Units

Controlled
Type Terms, Codelist
Role
CDISC Notes
Core
or Format
Char *
Grouping Used to define a further categorization of MBCAT.
Perm
Qualifier
Char
Result
Result of the Microbiology measurement or finding as originally received Exp
Qualifier or collected. Examples for GRAM STAIN findings: +3 MODERATE, +2
FEW, <10. Examples for CULTURE PLATE (ORGANISM) findings:
KLEBSIELLA PNEUMONIAE, STREPTOCOCCUS PNEUMONIAE
PENICILLIN RESISTANT.
Char (UNIT)
Variable Original unit for MBORRES. Example: mcg/mL
Perm
Qualifier

References
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.5.1
19H

120H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1

12H

123H

124H

MBSTRESC Character Result/Finding in Char
Std Format

Result
Qualifier

MBSTRESN Numeric Result/Finding in Num
Standard Units

Result
Qualifier

MBSTRESU Standard Units

Char (UNIT)
128H

Variable
Qualifier

Contains the result value for all findings, copied or derived from
Exp
MBORRES in a standard format or standard units. MBSTRESC should
store all results or findings in character format; if results are numeric, they
should also be stored in numeric format in MBSTRESN. For example, if a
test has results ―+3 MODERATE‖, ―MOD‖, and ―MODERATE‖ in
MBORRES and these results effectively have the same meaning, they could
be represented in standard format in MBSTRESC as ―MODERATE‖.
Used for continuous or numeric results or findings in standard format;
Perm
copied in numeric format from MBSTRESC. MBSTRESN should store
all numeric test results or findings.
Standardized unit used for MBSTRESC and MBSTRESN.
Perm

125H

126H

SDTM 2.2.3,
SDTMIG 4.1.5.1
127H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTMIG
Appendix C1
SDTM 2.2.3
129H

130H

MBRESCAT Result Category

Char *

Variable
Qualifier

MBSTAT

Char (ND)

Record
Qualifier

Completion Status

1965H

Used to categorize the result of a finding in a standard format. Example
for ORGANISM finding: INFECTING, COLONIZER,
CONTAMINANT, or NORMAL FLORA.
Used to indicate Microbiology was not done, or a test was not done.
Should be null or have a value of NOT DONE.

Exp

Perm

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3
13H

132H

13H

MBREASND Reason Microbiology Not Char
Performed

Record
Qualifier

Reason not done. Used in conjunction with MBSTAT when value is NOT Perm
DONE. Examples: BROKEN EQUIPMENT or SUBJECT REFUSED.

MBNAM

Record
Qualifier

Name or identifier of the laboratory or vendor who provides the test
results.

134H

135H

Vendor Name

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November 12, 2008

Char

Perm

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
MBLOINC

Variable Label
LOINC Code

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char *
Synonym 1. Dictionary-derived LOINC Code for MBTEST.
Qualifier 2. The sponsor is expected to provide the dictionary name and

Core

References

Perm

SDTM 2.2.3,
SDTMIG 4.1.3.2
136H

version used to map the terms utilizing the define.xml external
codelist attributes
MBSPEC

Specimen Type

Char *

MBSPCCND Specimen Condition

Char

MBLOC

Char (LOC)

Specimen Collection
Location

MBMETHOD Method of Test or
Examination
MBBLFL
Baseline Flag

137H

Char *
Char (NY)
196H

Record
Qualifier
Record
Qualifier
Record
Qualifier

Defines the type of specimen used for a measurement. Examples:
Perm
SPUTUM, BLOOD, PUS.
Free or standardized text describing the condition of the specimen.
Perm
Example: CONTAMINATED.
Location relevant to the collection of the measurement. Examples: LUNG,
Perm
VEIN, LEFT KNEE WOUND, ARM ULCER 1, RIGHT THIGH LATERAL

Record
Qualifier
Record
Qualifier

Method of the test or examination. Examples: GRAM STAIN,
CULTURE PLATE, BROTH.
Indicator used to identify a baseline value. The value should be ―Y‖ or
null.

SDTM 2.2.3
SDTM 2.2.3
SDTM 2.2.3,
SDTMIG
Appendix C1
SDTM 2.2.3

Exp
Perm

SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
138H

139H

MBDRVFL

VISITNUM

Derived Flag

Visit Number

Char (NY)
1967H

Num

Record
Qualifier

Timing

Used to indicate a derived record. The value should be Y or null. Records Perm
that represent the average of other records or some other derivation, and
those that do not come from the CRF, are examples of records that would
be derived for the submission datasets. If MBDRVFL=Y, then
MBORRES may be null with MBSTRESC and (if numeric) MBSTRESN
having the derived value.
1. Clinical encounter number.
Exp
2. Numeric version of VISIT, used for sorting.
140H

14H

142H

SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.5,
SDTMIG 7.4
SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.8
SDTM 2.2.5,
SDTMIG 4.1.4.4,
SDTMIG 4.1.4.6
143H

14H

VISIT

Visit Name

Char

Timing

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

145H

146H

VISITDY

Planned Study Day of Visit Num

Timing

147H

148H

MBDTC

Date/Time of Specimen
Collection

Char ISO 8601

Study Day of MB
Specimen Collection

Num

Timing

Exp
149H

150H

MBDY

Timing

1. Study day of the specimen collection, measured as integer days.
Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.

15H

152H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
MBTPT

Controlled
Type Terms, Codelist
Role
or Format
Planned Time Point Name Char
Timing
Variable Label

MBTPTNUM Planned Time Point
Number
MBELTM
Planned Elapsed Time
from Time Point Ref

MBTPTREF Time Point Reference

Num

Timing

Char ISO 8601

Timing

CDISC Notes

Core

1. Text Description of time when specimen should be taken.
Perm
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See MBTPTNUM and MBTPTREF.
Examples: Start, 5 min post.
Numerical version of MBTPT to aid in sorting.
Perm

References
SDTM 2.2.5,
SDTMIG 4.1.4.10
153H4

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTMIG 4.1.4.10
15H

Char

Timing

Planned elapsed time (in ISO 8601) relative to a planned fixed reference Perm
(MBTPTREF). This variable is useful where there are repetitive
measures. Not a clock time or a date time variable. Represented as an ISO
8601 duration. Examples: ―-PT15M‖ to represent the period of 15
minutes prior to the reference point indicated by MBTPTREF, or ―PT8H‖
to represent the period of 8 hours after the reference point indicated by
MBTPTREF.
Name of the fixed reference point referred to by MBELTM,
Perm
MBTPTNUM, and MBTPT. Example: PREVIOUS DOSE.
156H

157H

SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.10
158H

159H

MBRFTDTC Date/Time of Reference
Time Point

Char ISO 8601

Timing

Date/time of the reference time point, MBTPTREF.

Perm
160H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.9.2 ASSUMPTIONS FOR MICROBIOLOGY SPECIMEN (MB) DOMAIN MODEL
1.

Definition: The MB domain is designed to store microbiology findings that include organisms found, grain stain results and organism growth status.

MBSPID is used to uniquely identify an organism. MBSPID would remain the same each time the same organism is identified in a new specimen.
Often the original line number used to record the first occurrence of the organism is used again as an organism identifier when it is found in another
specimen. For example, MBSPID is 01 at visit 10 for organism ―STAPHYLOCOCCUS AUREUS‖. For the same organism at visit 30, MBSPID is
again 01.

MBTESTCD value for organisms present in a specimen is "ORGANISM".

MBDTC can be used to record the date/time that an organism started to grow in the culture, or the date/time that the culture became positive for the
organism.

MBGRPID is used to link to findings related to that organism in the MS domain. For example, if in Specimen 1, organism STREPTOCOCCUS
PNEUMONIAE PENICILLIN RESISTANT is found with MBGRPID=1, then findings such as susceptibility tests, colony count, etc. for that
organism in Specimen 1, would all have the same value of MSGRPID=1 in the MS domain. The use of GRPID to relate MS to MB greatly
simplifies RELREC because only two records are needed in RELREC to describe the relationship of MB to the many related records in MS. With
this method there is no need to create detailed relationships at the subject level.

MBRESCAT is expected in all records where a microorganism has been identified to differentiate between colonizing organisms and the one(s) that
are causing the infection. It is not expected when there is ―No growth‖ or when the results are from a gram stain.

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CDISC SDTM Implementation Guide (Version 3.1.2)
7.

The following Qualifiers would not generally be used in MB: --MODIFY, --BODSYS, --FAST, --TOX, --TOXGR --SEV.

6.3.9.3 MICROBIOLOGY SUSCEPTIBILITY (MS) DOMAIN MODEL
ms.xpt, Microbiology Susceptibility Test — Findings, Version 3.1.2. One record per microbiology susceptibility test (or other organism-related finding) per
organism found in MB, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char MS
Identifier Two-character abbreviation for the domain.
1968H

Core

References

Req
Req

SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
16H

162H

USUBJID

Unique Subject Identifier

Char

MSSEQ

Sequence Number

Num

MSGRPID

Group ID

Char

MSREFID

Reference ID

Char

MSSPID

Sponsor-Defined Identifier Char

Identifier

Identifier used to uniquely identify a subject across all studies for all
applications or submissions involving the product.
Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain to
support relationships within the domain and between domains. In MS,
used to link to organism in MB.
Identifier Internal or external specimen identifier. Example: Specimen ID.

Req

Identifier Sponsor-defined reference number. Perhaps pre-printed on the CRF as an
explicit line identifier or defined in the sponsor's operational database.
Topic
Short name of the measurement, test, or finding described in MSTEST. It
can be used as a column name when converting a dataset from a vertical
to a horizontal format. The value in MSTESTCD cannot be longer than 8
characters, nor can it start with a number (e.g.‖1TEST). MSTESTCD
cannot contain characters other than letters, numbers, or underscores.
Examples for GROWTH findings: EXTGROW, COLCOUNT. For
SUSCEPTIBILITY findings, the test is the drug the organism was tested
with, i.e. PENICLLN, AMOXCLLN.
Synonym Verbatim name of the test or examination used to obtain the measurement
Qualifier or finding. Examples for GROWTH findings: Extent of Growth, Colony
Count. Examples for SUSCEPTIBILITY findings: Amoxicillin
Susceptibility, Penicillin Susceptibility
Grouping Used to define a category of related records. Examples: GROWTH,
Qualifier SUSCEPTIBILITY.
Grouping A further categorization of a test category. Examples: CULTURE,
Qualifier ISOLATE

Perm

163H4

Req
Req

SDTM 2.2.4,
SDTMIG 4.1.2.6
165H

Perm

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.1.8
16H

MSTESTCD Microbiology Organism
Finding Short Name

MSTEST

MSCAT
MSSCAT

Char *

Organism Test or Finding Char *
Name

Category for Organism
Char *
Findings
Subcategory for Organism Char *
Findings

167H

Req
168H

169H

Req

170H

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
SDTM 2.2.3,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.2.6
17H

172H

173H

Req
174H

Perm
175H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
MSORRES

Variable Label
Result or Finding in
Original Units

MSORRESU Original Units

Controlled
Type Terms, Codelist
Role
or Format
Char
Result
Qualifier

Char (UNIT)
178H

Variable
Qualifier

CDISC Notes

Core

Result of the Microbiology Organism measurement or finding as
Exp
originally received or collected. Examples for GROWTH findings:
GROWTH INTO 3RD QUADRANT. Examples for SUSCEPTIBLITY
findings:.0080,.0023
Original units in which the data were collected. The unit for MSORRES. Exp
Example: mcg/mL

References
SDTM 2.2.3,
SDTMIG 4.1.5.1
176H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1
179H

180H

MSSTRESC Character Result/Finding in Char
Std Format

Result
Qualifier

MSSTRESN Numeric Result/Finding in Num
Standard Units

Result
Qualifier

MSSTRESU Standard Units

Char (UNIT)
184H

Variable
Qualifier

Contains the result value for all findings, copied or derived from
Exp
MSORRES in a standard format or standard units. MSSTRESC should
store all results or findings in character format; if results are numeric, they
should also be stored in numeric format in MSSTRESN. For example, if a
test has results ―+3 MODERATE‖, ―MOD‖, and ―MODERATE‖, and in
MSORRES and these results effectively have the same meaning, they
could be represented in standard format in MSSTRESC as
―MODERATE‖.
Used for continuous or numeric results or findings in standard format;
Exp
copied in numeric format from MSSTRESC. MSSTRESN should store all
numeric test results or findings.
Standardized unit used for MSSTRESC and MSSTRESN.
Exp
18H2

SDTM 2.2.3,
SDTMIG 4.1.5.1
183H

SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTMIG
Appendix C1
SDTM 2.2.3
185H

186H

MSRESCAT Result Category

Char *

Variable
Qualifier

MSSTAT

Char (ND)

Record
Qualifier

Completion Status

196H

Used to categorize the result of a finding in a standard format. Example Exp
for SUSCEPTIBILITY finding: SUSCEPTIBLE, INTERMEDIATE,
RESISTANT, or UNKNOWN.
Used to indicate a test on an organism was not done, or a test was not
Perm
performed. Should be null if a result exists in MSORRES or have a value
of NOT DONE.

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3
187H

18H

189H

MSREASND Reason Test Not Done

MSNAM

Vendor Name

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November 12, 2008

Char

Record
Qualifier
Record
Qualifier

Reason not done. Describes why a measurement or test was not
performed. Used in conjunction with MSSTAT when value is NOT
DONE. Example: SAMPLE LOST
Name or identifier of the laboratory or vendor that provided the test
results.

Perm
190H

19H

Perm

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
MSLOINC

Variable Label
LOINC Code

Controlled
Type Terms, Codelist
Role
CDISC Notes
or Format
Char *
Synonym 1. Dictionary-derived LOINC Code for MSTEST.
Qualifier 2. The sponsor is expected to provide the dictionary name and

Core

References

Perm

SDTM 2.2.3,
SDTMIG 4.1.3.2
192H

version used to map the terms utilizing the define.xml external
codelist attributes
MSMETHOD Method of Test or
Examination
MSBLFL
Baseline Flag

Char *
Char (NY)
1970H

Record
Qualifier
Record
Qualifier

Method of the test or examination. Example for SUSCEPTIBILITY:
ETEST, BROTH DILUTION.
Indicator used to identify a baseline value. The value should be ―Y‖ or
null.

Exp

SDTM 2.2.3

Perm

SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
193H

194H

MSDRVFL

VISITNUM

Derived Flag

Visit Number

Char (NY)
197H

Num

Record
Qualifier

Timing

Used to indicate a derived record. The value should be Y or null. Records Perm
that represent the average of other records or some other derivation, and
those that do not come from the CRF, are examples of records that would
be derived for the submission datasets. If MSDRVFL=Y, then MSORRES
may be null, with MSSTRESC and (if numeric) MSSTRESN having the
derived value.
1. Clinical encounter number.
Exp
2. Numeric version of VISIT, used for sorting.
195H

196H

197H

19H

VISIT

Visit Name

Char

Timing1.

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

120H

VISITDY

Planned Study Day of Visit Num

Timing

120H

1203H

MSDTC

Date/Time of Test

Char ISO 8601

Timing

Perm
1204H

1205H

MSDY

MSTPT

Study Day of Test

Num

Planned Time Point Name Char

MSTPTNUM Planned Time Point
Number

Num

Timing

1. Study day of the test, measured as integer days.
Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.
1. Text Description of time when test should be done.
Perm
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See MSTPTNUM and MSTPTREF.
Examples: Start, 5 min post.
Numerical version of MSTPT to aid in sorting.
Perm
1206H

1207H

SDTM 2.2.5,
SDTMIG 4.1.4.10
1208

SDTM 2.2.5,
SDTMIG 4.1.4.10
1209H

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Variable
Name
MSELTM

Variable Label
Planned Elapsed Time
from Time Point Ref

MSTPTREF Time Point Reference

Controlled
Type Terms, Codelist
Role
or Format
Char ISO 8601
Timing

Char

Timing

CDISC Notes

Core

Elapsed time (in ISO 8601) relative to a planned fixed reference
Perm
(MSTPTREF). This variable is useful where there are repetitive measures.
Not a clock time or a date time variable. Examples: ―-PT15M‖ to
represent the period of 15 minutes prior to the reference point indicated by
MSTPTREF, or ―P8H‖ to represent the period of 8 hours after the
reference point indicated by MSTPTREF.
Name of the fixed reference point referred to by MSELTM,
Perm
MSTPTNUM, and MSTPT. Example: PREVIOUS DOSE.

References
SDTM 2.2.5,
SDTMIG 4.1.4.3,
SDTMIG 4.1.4.10
120H

12H

SDTM 2.2.5,
SDTMIG 4.1.4.3
SDTMIG 4.1.4.10
12H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

6.3.9.4 ASSUMPTIONS FOR MICROBIOLOGY SUSCEPTIBILITY (MS) DOMAIN MODEL
1.

Definition: The MS domain is designed to store any findings related to the organisms found and submitted in MB. This will usually consist of
susceptibility testing results, but can also be other organism-related findings such as extent of growth of an organism. This domain is intended to be
used in conjunction with the MB domain described above.

The following Qualifiers would not generally be used in MB: --MODIFY, --BODSYS, --SPEC, --SPCCND, --FAST, --TOX, --TOXGR --SEV.

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CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.9.5 EXAMPLES FOR MB AND MS DOMAIN MODELS
Example 1: MB specimen findings
Rows 1, 2:
Show gram stain results for Specimen 1 (MBREFID=SP01).
Rows 3, 4:
Show organisms found in specimen 1 at visit 1. The MBGRPID is used to link these organisms to findings about these organisms in MS,
by MSGRPID.
Row 5:
Shows the organism assigned as ORG02 is still present in Specimen 2 at Visit 2.
Row 6:
Shows no organisms have grown at Visit 3. Therefore the organism recorded is "NO GROWTH".
Row 1-6:
Show MBMETHOD being used for reporting the method of testing the sample, e.g. GRAM STAIN or CULTURE PLATE.
Microbiology Example 1 MB dataset
Row STUDYID DOMAIN
USUBJID
ABC
MB
ABC-001-001
1
ABC
MB
ABC-001-001
2

MBSEQ MBGRPID MBREFID
1
SP01
2
SP01

MBSPID

MBTESTCD
GMNCOC
GMNROD

MBTEST
Gram Negative Cocci
Gram Negative Rods

ABC

ABC-001-001

SP01

ORG01

ORGANISM

Organism Present

4
5
6

ABC
ABC
ABC

MB
MB
MB

ABC-001-001
ABC-001-001
ABC-001-001

4
5
6

2
3

SP01
SP02
SP03

ORG02
ORG02
ORG03

ORGANISM
ORGANISM
ORGANISM

Organism Present
Organism Present
Organism Present

Row
1 (cont)
2 (cont)

MBSTRESC
FEW
FEW

3 (cont)

STREPTOCOCCUS PNEUMONIAE,
PENICILLIN RESISTANT

4 (cont)
5 (cont)
6 (cont)

KLEBSIELLA PNEUMONIAE
KLEBSIELLA PNEUMONIAE
NO GROWTH

MBRESCAT

MBLOC MBSPEC
LUNG
SPUTUM
LUNG
SPUTUM

MBORRES
2+ FEW
2+ FEW
STREPTOCOCCUS PNEUMONIAE
PENICILLIN RESISTANT
KLEBSIELLA PNEUMONIAE
KLEBSIELLA PNEUMONIAE
NO GROWTH

MBSPCCND
MUCOID
MUCOID

MBMETHOD
GRAM STAIN
GRAM STAIN

VISITNUM
1
1

MBDTC
2005-06-19T08:00
2005-06-19T08:00

INFECTING

LUNG

SPUTUM

MUCOID

CULTURE PLATE

2005-06-19T08:00

COLONIZER
COLONIZER

LUNG
LUNG
LUNG

SPUTUM
SPUTUM
SPUTUM

MUCOID

CULTURE PLATE
CULTURE PLATE
CULTURE PLATE

1
2
3

2005-06-19T08:00
2005-06-26T08:00
2005-07-06T08:00

If the method of the collection of the sputum is reported (e.g., EXPECTORATION or BIOPSY), this information would go into SUPPMB, since MBMETHOD
refers to the method used to obtain the results.
suppmb.xpt
Row STUDYID RDOMAIN
USUBJID
ABC
MB
ABC-001-001
1

IDVAR IDVARVAL
QNAM
MBSEQ
1
COLMETH

QLABEL
Collection Method

QVAL
EXPECTORATION

QORIG
CRF

QEVAL

Example 2: MS – Findings about organisms from Example 1, related within USUBJID, by MBGRPID=MSGRPID
Row 1:
Shows extent of growth of Organism 1 found at Visit 1 in specimen 1 (MBGRPID=1, Row 3 in MB example above).
Rows 2, 3:
Show results of susceptibility testing on Organism 1 found at Visit 1 in specimen 1 (MBGRPID=1, Row 3 in MB example above).
Row 4:
Shows extent of growth of Organism 2 found at Visit 1 in specimen 1 (MBGRPID=2, Row 4 in MB example above).
Rows 5, 6:
Show results of susceptibility testing on Organism 2 found at Visit 1 in specimen 1 (MBGRPID=2, Row 4 in MB example above).
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Final

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CDISC SDTM Implementation Guide (Version 3.1.2)
Row 7:
Row
1
2
3
4
5
6
7

Shows results of susceptibility testing on Organism 2 found at Visit 1 in specimen 2 (MBGRPID=3, Row 5 in MB example above).
STUDYID
ABC
ABC
ABC
ABC
ABC
ABC
ABC

DOMAIN
MS
MS
MS
MS
MS
MS
MS

Row

MSORRES

1 (cont)

IN 2ND QUADRANT

2 (cont)
3 (cont)

0.004
0.023
>=30 COLONIES IN 2ND
QUADRANT
0.125
0.023
0.026

4 (cont)
5 (cont)
6 (cont)
7 (cont’d)

USUBJID
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
ABC-001-001
MSORRESU

mcg/mL
mcg/mL

mcg/mL
mcg/mL
mcg/mL

MSSEQ
1
2
3
4
5
6
7

MSGRPID
1
1
1
2
2
2
3

MSTESTCD
EXTGROW
DRUGA
PENICLLN
EXTGROW
DRUGA
PENICLLN
PENICLLN

MSSTRESC
MSSTRESN
IN 2ND
QUADRANT
0.004
0.004
0.023
0.023
>=30 COLONIES IN
2ND QUADRANT
0.125
0.125
0.023
0.023
0.026
0.026

MSTEST
Extent of Growth
Sponsor Drug
Penicillin
Extent of Growth
Sponsor Drug
Penicillin
Penicillin

MSSTRESU

MSRESCAT

MSCAT
GROWTH
SUSCEPTIBILITY
SUSCEPTIBILITY
GROWTH
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY

MSMETHOD

VISITNUM
1

mcg/mL
mcg/mL

SUSCEPTIBLE
RESISTANT

E-TEST
E-TEST

1
1
1

mcg/mL
mcg/mL
mcg/mL

SUSCEPTIBLE
INTERMEDIATE
INTERMEDIATE

E-TEST
E-TEST
E-TEST

1
1
2

Example 3: MB with multiple labs
Row 1, 2:
Show the same organism identified by a central and a local lab. Note that MBSPID is different for each lab, and also MBGRPID is different for
each lab. This is because the organism is found and tracked separately for each lab although it came from the same specimen.
Row

STUDYID

DOMAIN

USUBJID

MBSEQ

MBGRPID

MBREFID

MBSPID

MBTESTCD

ABC

ABC-001-002

SPEC01

ORG01

ORGANISM

Organism Present

ABC

ABC-001-002

SPEC01

ORG02

ORGANISM

Organism Present

Row

MBORRES

MBSTRESC

MBRESCAT

1 (cont)

ENTEROCOCCUS
FAECALIS

INFECTING

2 (cont)

ENTEROCOCCUS
FAECALIS

INFECTING

MBNAM

MBLOC

MBSPEC

CENTRAL

SKIN SITE 1

FLUID

LOCAL

SKIN SITE 1

FLUID

MBTEST

MBMETHOD

VISITNUM

MBDTC

CULTURE PLATE

2005-07-21T08:00

CULTURE PLATE

2005-07-21T08:00

Example 4: MS – findings about organisms from Example 3, multiple labs
Rows 1, 2: Show susceptibility test results done by the central lab, for the organism identified by the central lab where MBGRPID=1 in Row 1 of Example 3 above.
Note that the central lab performed only one method of susceptibility testing (the E-TEST) for the two drugs, Sponsor and Amoxicillin.

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CDISC SDTM Implementation Guide (Version 3.1.2)
Rows 3-8: Show susceptibility test results done by the local lab, for the organism identified by the local lab where MBGRPID=2 in Row 2 of Example 3 above.
Note that the local lab has performed three different methods of susceptibility testing (Broth Dilution, Zone Size, and E-TEST) for two drugs, thus
providing six records for MSGRPID=2.
Row STUDYID
ABC
1
ABC
2
ABC
3
ABC
4
ABC
5
ABC
6
ABC
7
ABC
8
Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)

DOMAIN
MS
MS
MS
MS
MS
MS
MS
MS

MSORRES
0.25
1
0.5
0.5
23
25
0.25
1

USUBJID
ABC-001-002
ABC-001-002
ABC-001-002
ABC-001-002
ABC-001-002
ABC-001-002
ABC-001-002
ABC-001-002

MSORRESU
mcg/mL
mcg/mL
mcg/mL
mcg/mL
mm
mm
mcg/mL
mcg/mL

MSSEQ
1
2
3
4
5
6
7
8

MSSTRESC
0.25
1
0.5
0.5
23
25
0.25
1

MSGRPID
1
1
2
2
2
2
2
2

MSSTRESN
0.25
1
0.5
0.5
23
25
0.25
1

MSREFID
CENTABC
CENTABC
LOCXYZ
LOCXYZ
LOCXYZ
LOCXYZ
LOCXYZ
LOCXYZ
MSSTRESU
mcg/mL
mcg/mL
mcg/mL
mcg/mL
mm
mm
mcg/mL
mcg/mL

MSTESTCD
DRUGA
AMOXCLAV
DRUGA
AMOXCLAV
DRUGA
AMOXCLAV
DRUGA
AMOXCLAV
MSRESCAT
SUSCEPTIBLE
RESISTANT
SUSCEPTIBLE
RESISTANT
SUSCEPTIBLE
RESISTANT
SUSCEPTIBLE
RESISTANT

MSTEST
Sponsor Drug
Amoxicillin / Clavulanate
Sponsor Drug
Amoxicillin / Clavulanate
Sponsor Drug
Amoxicillin / Clavulanate
Sponsor Drug
Amoxicillin / Clavulanate
MSMETHOD
E-TEST
E-TEST
BROTH DILUTION
BROTH DILUTION
ZONE SIZE
ZONE SIZE
E-TEST
E-TEST

MSCAT
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY
SUSCEPTIBILITY

VISITNUM
1
1
1
1
1
1
1
1

Example 5: RELREC to relate MB and MS
Rows 1, 2: Show the one-to-many relationship between MB and MS. For any organism found in a microbiology specimen and recorded in MB, there may be
multiple findings about that organism recorded in MS. The organism in MB can be linked to its findings in MS because the value assigned to
MBGRPID = the value assigned to MSGRPID for any organism within a subject.
Row
1
2

RDOMAIN
MB
MS

USUBJID

IDVAR
MBGRPID
MSGRPID

IDVARVAL

RELTYPE
ONE
MANY

RELID
A
A

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CDISC SDTM Implementation Guide (Version 3.1.2)

6.3.10 PHARMACOKINETICS DOMAINS — PC AND PP
pc.xpt, Pharmacokinetic Concentrations — Findings, Version 3.1.2. One record per sample characteristic or time-point concentration per reference time
point or per analyte per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char PC
Identifier Two-character abbreviation for the domain.
1972H

Core
Req
Req

Reference
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG
Appendix C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
123H

124H

USUBJID

Unique Subject Identifier Char

Identifier Unique subject identifier within the submission.

Req

PCSEQ

Sequence Number

Num

Req

PCGRPID

Group ID

Char

PCREFID

Reference ID

Char

Identifier Sequence Number given to ensure uniqueness of subject records within a
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain to support
relationships within the domain and between domains.
Identifier Internal or external specimen identifier. Example: Specimen ID.

PCSPID

Sponsor-Defined
Identifier
Pharmacokinetic Test
Short Name

Char

Identifier Sponsor-defined reference number.

Perm

Char

Topic

Req

125H6

Perm

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.3,
SDTMIG 4.1.1.8,
SDTMIG 4.1.2.1
127H

Perm
128H

PCTESTCD

PCTEST

Pharmacokinetic Test
Name

129H

Char

Synonym
Qualifier

PCCAT

Test Category

Char

PCSCAT

Test Subcategory

Char

PCORRES

Result or Finding in
Original Units
PCORRESU Original Units

Char
Char

(UNIT)
1230H

Short name of the analyte or specimen characteristic. It can be used as a
column name when converting a dataset from a vertical to a horizontal format.
The value in PCTESTCD cannot be longer than 8 characters, nor can it start
with a number (e.g., ―1TEST‖). PCTESTCD cannot contain characters other
than letters, numbers, or underscores. Examples: ASA, VOL, SPG.
Name of the analyte or specimen characteristic. Note any test normally
performed by a clinical laboratory is considered a lab test. The value in
PCTEST cannot be longer than 40 characters. Examples: Acetylsalicylic Acid,
Volume, Specific Gravity.
Used to define a category of related records. Examples: ANALYTE,
SPECIMEN PROPERTY.
A further categorization of a test category.

Grouping
Qualifier
Grouping
Qualifier
Result
Result of the measurement or finding as originally received or collected.
Qualifier
Variable Original units in which the data were collected. The unit for PCORRES.
Qualifier Example: mg/L.

120H

12H

Req

124H

125H

Perm
126H

Perm
127H

Exp
128H9

Exp
123H

123H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
PCSTRESC

PCSTRESN

PCSTRESU

Controlled
Type Terms, Codelist Role
CDISC Notes
Core
or Format
Character Result/Finding Char
Result
Contains the result value for all findings, copied or derived from PCORRES in Exp
in Std Format
Qualifier a standard format or standard units. PCSTRESC should store all results or
findings in character format; if results are numeric, they should also be stored
in numeric format in PCSTRESN. For example, if a test has results ―NONE‖,
―NEG‖, and ―NEGATIVE‖ in PCORRES and these results effectively have the
same meaning, they could be represented in standard format in PCSTRESC as
―NEGATIVE‖. For other examples, see general assumptions.
Numeric Result/Finding Num
Result
Used for continuous or numeric results or findings in standard format; copied Exp
in Standard Units
Qualifier in numeric format from PCSTRESC. PCSTRESN should store all numeric test
results or findings.
Variable Label

Standard Units

Char

(UNIT)
1236H

Variable Standardized unit used for PCSTRESC and PCSTRESN.
Qualifier

Exp

Record Used to indicate a result was not obtained. Should be null if a result exists in
Qualifier PCORRES.

Perm

Reference
SDTM 2.2.3,
SDTMIG 4.1.5.1
123H

1234H

SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
SDTM 2.2.3
1235H

1237H

1238H

PCSTAT

Completion Status

Char

(ND)
1973H

1239H

1240H

124H

PCREASND Reason Test Not Done

Char

Record Describes why a result was not obtained such as SPECIMEN LOST. Used in
Qualifier conjunction with PCSTAT when value is NOT DONE.

Perm

Record
Qualifier
Record
Qualifier
Record
Qualifier
Record
Qualifier

Exp

124H

1243H

PCNAM

Vendor Name

Char

PCSPEC

Specimen Material Type Char

PCSPCCND Specimen Condition

Char

PCMETHOD Method of Test or
Examination

Char

PCFAST

Char

(NY)

PCDRVFL

Fasting Status

Derived Flag

Char

1974H

(NY)
1975H

Record
Qualifier

Name or identifier of the laboratory or vendor who provides the test results.

Defines the type of specimen used for a measurement. Examples: SERUM,
Req
PLASMA, URINE.
Free or standardized text describing the condition of the specimen e.g.
Perm
HEMOLYZED, ICTERIC, LIPEMIC etc.
Method of the test or examination. Examples include HPLC/MS, ELISA. This Perm
should contain sufficient information and granularity to allow differentiation of
various methods that might have been used within a study.
Indicator used to identify fasting status.
Perm

SDTM 2.2.3
SDTM 2.2.3

SDTM 2.2.3,
SDTMIG
Appendix C1
SDTM 2.2.3,
SDTMIG 4.1.3.7,
SDTMIG 4.1.5.1,
SDTMIG
Appendix C1
124H

Record Used to indicate a derived record. The value should be Y or null. Records that Perm
Qualifier represent the average of other records, which do not come from the CRF, are
examples of records that would be derived for the submission datasets. If
PCDRVFL=Y, then PCORRES may be null with PCSTRESC, and (if numeric)
PCSTRESN having the derived value.

SDTM 2.2.3

1245H

1246H

1247H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
PCLLOQ
VISITNUM

Variable Label
Lower Limit of
Quantitation
Visit Number

Controlled
Type Terms, Codelist Role
CDISC Notes
or Format
Num
Variable Indicates the lower limit of quantitation for an assay. Units should be those
Qualifier used in PCSTRESU.
Num
Timing 1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Exp

SDTM 2.2.3

Exp

Char

1. Protocol-defined description of clinical encounter
2. May be used in addition to VISITNUM and/or VISITDY

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

Core

Reference

1248H

1249H

VISIT

Visit Name

Timing

1250H

125H

VISITDY

Planned Study Day of
Visit

Num

Date/Time of Specimen
Collection

Char

End Date/Time of
Specimen Collection

Char

Actual Study Day of
Specimen Collection

Num

Planned Time Point
Name

Char

Timing

125H

1253H

PCDTC

ISO 8601

Timing

Date/time of specimen collection represented in ISO 8601 character format. If Exp
there is no end time, then this will be the collection time.
1254H

125H

PCENDTC

PCDY

PCTPT

PCTPTNUM Planned Time Point
Number
PCELTM
Planned Elapsed Time
from Time Point Ref

Num

PCTPTREF

Char

Time Point Reference

ISO 8601

Timing

End date/time of specimen collection represented in ISO 8601 character
format. If there is no end time, the collection time should be stored in PCDTC,
and PCENDTC should be null.
1. Study day of specimen collection, measured as integer days.
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics.
1. Text Description of time when specimen should be taken.
2. This may be represented as an elapsed time relative to a fixed reference
point, such as time of last dose. See PCTPTNUM and PCTPTREF. Examples:
Start, 5 min post.
Numerical version of PCTPT to aid in sorting.

Perm
1256H

1257H

Perm
1258H

1259H

Perm
1260H

Perm

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.3,
SDTMIG 4.1.4.10
126H

Char

PCRFTDTC Date/Time of Reference Char
Point
PCEVLINT Evaluation Interval
Char

ISO 8601

Timing

Timing
ISO 8601

Timing

ISO 8601

Timing

Planned elapsed time (in ISO 8601) relative to a planned fixed reference
Perm
(PCTPTREF) such as “PREVIOUS DOSE” or “PREVIOUS MEAL”. This
variable is useful where there are repetitive measures. Not a clock time or a date
time variable.
Name of the fixed reference point used as a basis for PCTPT, PCTPTNUM,
Perm
and PCELTM. Example: Most Recent Dose.
Date/time of the reference time point described by PCTPTREF.
Perm
126H

1263H

SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5,
SDTMIG 4.1.4.10
SDTM 2.2.5
1264H

1265H

Evaluation Interval associated with a PCTEST record represented in ISO 8601 Perm
character format. Example: "-P2H" to represent an interval of 2 hours prior to a
PCTPT.

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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CDISC SDTM Implementation Guide (Version 3.1.2)
pp.xpt, Pharmacokinetic Parameters — Findings, Version 3.1.2,. One record per PK parameter per time-concentration profile per subject, Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist Role
CDISC Notes
or Format
Char
Identifier Unique identifier for a study.
Char PP
Identifier Two-character abbreviation for the domain.
1976H

Core

References

Req SDTM 2.2.4
Req SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG Appendix C2
Identifier Unique subject identifier within the submission.
Req SDTM 2.2.4,
SDTMIG 4.1.2.3
Identifier Sequence Number given to ensure uniqueness of subject records within a Req SDTM 2.2.4
domain. May be any valid number.
Identifier Used to tie together a block of related records in a single domain to
Perm SDTM 2.2.4,
support relationships within the domain and between domains.
SDTMIG 4.1.2.6
Topic
Short name of the pharmacokinetic parameter. It can be used as a column
Req SDTM 2.2.3,
name when converting a dataset from a vertical to a horizontal format. The value
SDTMIG 4.1.1.8
in PPTESTCD cannot be longer than 8 characters, nor can it start with a number
SDTMIG 4.1.2.1
(e.g., ―1TEST‖). PPTESTCD cannot contain characters other than letters,
numbers, or underscores. Examples: AUC, TMAX, CMAX.
Synonym Name of the pharmacokinetic parameter. The value in PPTEST cannot be Req SDTM 2.2.3,
Qualifier longer than 40 characters. Examples: AUC, Tmax, Cmax.
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
Grouping Used to define a category of related records. For PP, this should be the name Exp SDTM 2.2.3,
Qualifier of the analyte in PPTEST whose profile the parameter is associated with.
SDTMIG 4.1.2.6
Grouping Categorization of the model type used to calculate the PK parameters.
Perm SDTM 2.2.3,
Qualifier Examples include COMPARTMENTAL, NON-COMPARTMENTAL.
SDTMIG 4.1.2.6
Result Result of the measurement or finding as originally received or collected. Exp SDTM 2.2.3,
Qualifier
SDTMIG 4.1.5.1
Variable Original units in which the data were collected. The unit for PPORRES. Exp SDTM 2.2.3,
Qualifier Example: ng/L.
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1,
SDTMIG Appendix C1
Result Contains the result value for all findings, copied or derived from PPORRES in Exp SDTM 2.2.3,
Qualifier a standard format or standard units. PPSTRESC should store all results or
SDTMIG 4.1.5.1
findings in character format; if results are numeric, they should also be stored
in numeric format in PPSTRESN.
Result Used for continuous or numeric results or findings in standard format;
Exp SDTM 2.2.3,
Qualifier copied in numeric format from PPSTRESC. PPSTRESN should store all
SDTMIG 4.1.5.1
numeric test results or findings.
126H

1267H

USUBJID

Unique Subject Identifier Char

PPSEQ

Sequence Number

Num

PPGRPID

Group ID

Char

PPTESTCD

Parameter Short Name

Char

1268H9

1270H

127H

1273H

PPTEST

Parameter Name

Char

1274H

1275H

1276H

PPCAT

Parameter Category

Char

PPSCAT

Parameter Subcategory

Char

127H

1278H

PPORRES

Result or Finding in
Original Units
PPORRESU Original Units

Char

1279H

Char

(UNIT)
128H

1280H

128H

1283H

PPSTRESC

PPSTRESN

Character Result/Finding Char
in Std Format

Numeric Result/Finding Num
in Standard Units

1284H

1285H

1286H

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CDISC SDTM Implementation Guide (Version 3.1.2)

Variable
Name
PPSTRESU

Variable Label
Standard Units

Controlled
Type Terms, Codelist Role
CDISC Notes
or Format
Char (UNIT)
Variable Standardized unit used for PPSTRESC and PPSTRESN.
Qualifier
1287H

Core

References

Exp SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1,
SDTMIG Appendix C1
Perm SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG Appendix C1
Perm SDTM 2.2.3,
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
Exp SDTM 2.2.3
128H

1289H

PPSTAT

Completion Status

Char

(ND)
197H

Record Used to indicate that a parameter was not calculated. Should be null if a
Qualifier result exists in PPORRES.

1290H

129H

PPREASND Reason Parameter Not
Calculated

Char

Record Describes why a parameter was not calculated, such as INSUFFICIENT
Qualifier DATA. Used in conjunction with PPSTAT when value is NOT DONE.

1293H

1294H

PPSPEC

Specimen Material Type Char

PPDTC

Date/Time of Parameter Char
Calculations

ISO 8601

Date/Time of Reference Char
Point

ISO 8601

Record Defines the type of specimen used for a measurement. If multiple specimen
Qualifier types are used for a calculation (e.g., serum and urine for renal clearance), then
this field should be left blank. Examples: SERUM, PLASMA, URINE.
Timing Nominal date/time of parameter calculations.

Perm SDTM 2.2.5,
SDTMIG 4.1.4.1,
SDTMIG 4.1.4.8
Exp SDTM 2.2.5,
SDTMIG 4.1.4.10
1295H

1296H

PPRFTDTC

Timing

Date/time of the reference time point from the PC records used to
calculate a parameter record. The values in PPRFTDTC should be the
same as that in PCRFTDTC for related records.

1297H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
6.3.10.1

ASSUMPTIONS FOR PHARMACOKINETIC CONCENTRATIONS (PC) DOMAIN MODEL

PC Definition: Data collected about tissue (e.g., serum or plasma) concentrations of analytes (usually study drugs and/or their metabolites) as a function
of time after dosing the study drug.

The structure is one record per concentration or sample characteristic per analyte. In addition to one record for each concentration measurement,
specimen properties (e.g., volume and pH) are handled via separate records in this dataset.

Due to space limitations, not all expected or permissible Findings variables are included in the example.

The following Qualifiers would not generally be used in PC: --BODSYS, --SEV.
6.3.10.2

EXAMPLES FOR PHARMACOKINETIC CONCENTRATIONS (PC) DOMAIN MODEL

Example 1
This example shows concentration data for Drug A and metabolite of Drug A from plasma and from urine (shaded rows) samples collected pre-dose and after
dosing on two different study days, Days 1 and 11.

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CDISC SDTM Implementation Guide (Version 3.1.2)
All Rows:

PCTPTREF is a text value of the description of a “zero” time (e.g. time of dosing). It should be meaningful. If there are
multiple PK profiles being generated, the zero time for each will be different (e.g., a different dose such as "first dose",
"second dose") and, as a result, values for PCTPTREF must be different. In this example it is required to make values of
PCTPTNUM and PCTPT unique (See Section 4.1.4.10).
Rows 5, 6, 19, 20, 25, 26, 29, and 30: Specimen properties (VOLUME and PH) are submitted as values of PCTESTCD in separate rows.
Rows 3-6, 17-20, 23-30:
The elapsed times for urine samples are based upon the elapsed time (from the reference time point, PCTPTREF) for the end of the specimen
collection period. Elapsed time values that are the same for urine and plasma samples have been assigned the same value for PCTPT. For the
urine samples, the value in PCEVLINT describes the planned evaluation (or collection) interval relative to the time point. The
actual evaluation interval can be determined by subtracting PCDTC from PCENDTC.
1298H

Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

STUDYID DOMAIN USUBJID PCSEQ PCGRPID PCREFID PCTESTCD
ABC-123
PC
123-0001
1
Day 1
A554134-10 DRGA_MET
ABC-123
PC
123-0001
2
Day 1
A554134-10 DRGA_PAR
ABC-123
PC
123-0001
3
Day 1
A554134-11 DRGA_MET
ABC-123
PC
123-0001
4
Day 1
A554134-11 DRGA_PAR
ABC-123
PC
123-0001
5
Day 1
A554134-11 VOLUME
ABC-123
PC
123-0001
6
Day 1
A554134-11
PH
ABC-123
PC
123-0001
7
Day 1
A554134-12 DRGA_MET
ABC-123
PC
123-0001
8
Day 1
A554134-12 DRGA_PAR
ABC-123
PC
123-0001
9
Day 1
A554134-13 DRGA_MET
ABC-123
PC
123-0001
10
Day 1
A554134-13 DRGA_PAR
ABC-123
PC
123-0001
11
Day 1
A554134-14 DRGA_MET
ABC-123
PC
123-0001
12
Day 1
A554134-14 DRGA_PAR
ABC-123
PC
123-0001
13
Day 11 A554134-15 DRGA_MET
ABC-123
PC
123-0001
14
Day 11 A554134-15 DRGA_PAR
ABC-123
PC
123-0001
15
Day 11 A554134-16 DRGA_MET
ABC-123
PC
123-0001
16
Day 11 A554134-16 DRGA_PAR
ABC-123
PC
123-0001
17
Day 11 A554134-17 DRGA_MET
ABC-123
PC
123-0001
18
Day 11 A554134-17 DRGA_PAR
ABC-123
PC
123-0001
19
Day 11 A554134-17 VOLUME
ABC-123
PC
123-0001
20
Day 11 A554134-17
PH
ABC-123
PC
123-0001
21
Day 11 A554134-18 DRGA_MET
ABC-123
PC
123-0001
22
Day 11 A554134-18 DRGA_PAR
ABC-123
PC
123-0001
23
Day 11 A554134-19 DRGA_MET
ABC-123
PC
123-0001
24
Day 11 A554134-19 DRGA_PAR
ABC-123
PC
123-0001
25
Day 11 A554134-19 VOLUME
ABC-123
PC
123-0001
26
Day 11 A554134-19
PH
ABC-123
PC
123-0001
27
Day 11 A554134-20 DRGA_MET
ABC-123
PC
123-0001
28
Day 11 A554134-20 DRGA_PAR
ABC-123
PC
123-0001
29
Day 11 A554134-20 VOLUME
ABC-123
PC
123-0001
30
Day 11 A554134-20
PH
ABC-123
PC
123-0001
31
Day 11 A554134-21 DRGA_MET
ABC-123
PC
123-0001
32
Day 11 A554134-21 DRGA_PAR

PCTEST
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Volume
PH
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Volume
PH
Drug A Metabolite
Drug A Parent
Drug A Metabolite
Drug A Parent
Volume
PH
Drug A Metabolite
Drug A Parent
Volume
PH
Drug A Metabolite
Drug A Parent

PCCAT
ANALYTE
ANALYTE
ANALYTE
ANALYTE
SPECIMEN
SPECIMEN
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
SPECIMEN
SPECIMEN
ANALYTE
ANALYTE
ANALYTE
ANALYTE
SPECIMEN
SPECIMEN
ANALYTE
ANALYTE
SPECIMEN
SPECIMEN
ANALYTE
ANALYTE

PCSPEC PCORRES PCORRESU PCSTRESCPCSTRESNPCSTRESU
PLASMA
<0.1
ng/mL
<0.1
ng/mL
PLASMA
<0.1
ng/mL
<0.1
ng/mL
URINE
<2
ng/mL
<2
ng/mL
URINE
<2
ng/mL
<2
ng/mL
URINE
3500
mL
100
100
mL
URINE
5.5
5.5
5.5
PLASMA
5.4
ng/mL
5.4
5.4
ng/mL
PLASMA
4.74
ng/mL
4.74
4.74
ng/mL
PLASMA
5.44
ng/mL
5.44
5.44
ng/mL
PLASMA
1.09
ng/mL
1.09
1.09
ng/mL
PLASMA
PLASMA
<0.1
ng/mL
<0.1
ng/mL
PLASMA
3.41
ng/mL
3.41
3.41
ng/mL
PLASMA
<0.1
ng/mL
<0.1
ng/mL
PLASMA
8.74
ng/mL
8.74
8.74
ng/mL
PLASMA
4.2
ng/mL
4.2
4.2
ng/mL
URINE
245
ng/mL
245
245
ng/mL
URINE
13.1
ng/mL
13.1
13.1
ng/mL
URINE
574
mL
574
574
mL
URINE
5.5
5.5
5.5
PLASMA
9.02
ng/mL
9.02
9.02
ng/mL
PLASMA
1.18
ng/mL
1.18
1.18
ng/mL
URINE
293
ng/mL
293
293
ng/mL
URINE
7.1
ng/mL
7.1
7.1
ng/mL
URINE
363
mL
363
363
mL
URINE
5.5
5.5
5.5
URINE
280
ng/mL
280
280
ng/mL
URINE
2.4
ng/mL
2.4
2.4
ng/mL
URINE
606
mL
606
606
mL
URINE
5.5
5.5
5.5
PLASMA
3.73
ng/mL
3.73
3.73
ng/mL
PLASMA
<0.1
ng/mL
<0.1
ng/mL

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(PC dataset for example 1, continued)
Row
PCSTAT PCLLOQ VISITNUM VISIT VISITDY
PCDTC
PCENDTC
PCDY PCTPT PCTPTNUM PCTPTREF
PCRFTDTC
PCELTM PCEVLINT
0.10
1
DAY 1
1
2001-02-01T07:45
1
PREDOSE
0
Day 1 Dose 2001-02-01T08:00 -PT15M
1 (cont)
0.10
1
DAY 1
1
2001-02-01T07:45
1
PREDOSE
0
Day 1 Dose 2001-02-01T08:00 -PT15M
2 (cont)
2.00
1
DAY 1
1
2001-02-01T07:45 2001-02-01T07:45
1
PREDOSE
0
Day 1 Dose 2001-02-01T08:00 -PT15M
3 (cont)
2.00
1
DAY 1
1
2001-02-01T07:45 2001-02-01T07:45
1
PREDOSE
0
Day 1 Dose 2001-02-01T08:00 -PT15M
4 (cont)
1
DAY 1
1
2001-02-01T07:45 2001-02-01T07:45
1
PREDOSE
0
Day 1 Dose 2001-02-01T08:00 -PT15M
5 (cont)
1
DAY 1
1
2001-02-01T07:45 2001-02-01T07:45
1
PREDOSE
0
Day 1 Dose 2001-02-01T08:00 -PT15M
6 (cont)
0.10
1
DAY 1
1
2001-02-01T09:30
1
1H30MIN
1.5
Day 1 Dose 2001-02-01T08:00 PT1H30M
7 (cont)
0.10
1
DAY 1
1
2001-02-01T09:30
1
1H30MIN
1.5
Day 1 Dose 2001-02-01T08:00 PT1H30M
8 (cont)
0.10
1
DAY 1
1
2001-02-01T14:00
1
6H
6
Day 1 Dose 2001-02-01T08:00 PT6H00M
9 (cont)
0.10
1
DAY 1
1
2001-02-01T14:00
1
6H
6
Day 1 Dose 2001-02-01T08:00 PT6H
10 (cont)
2
DAY 2
2
2001-02-02T08:00
2
24H
24
Day 1 Dose 2001-02-01T08:00 PT24H
11 (cont) NOT DONE
0.10
2
DAY 2
2
2001-02-02T08:00
2
24H
24
Day 1 Dose 2001-02-01T08:00 PT24H
12 (cont)
0.10
3
DAY 11
11
2001-02-11T07:45
11 PREDOSE
0
Day 11 Dose 2001-02-11T08:00 -PT15M
13 (cont)
0.10
3
DAY 11
11
2001-02-11T07:45
11 PREDOSE
0
Day 11 Dose 2001-02-11T08:00 -PT15M
14 (cont)
0.10
3
DAY 11
11
2001-02-11T09:30
11
1H30MIN
1.5
Day 11 Dose 2001-02-11T08:00 PT1H30M
15 (cont)
0.10
3
DAY 11
11
2001-02-11T09:30
11
1H30MIN
1.5
Day 11 Dose 2001-02-11T08:00 PT1H30M
16 (cont)
2.00
3
DAY 11
11
2001-02-11T08:00 2001-02-11T14:03 11
6H
6
Day 11 Dose 2001-02-11T08:00 PT6H
-PT6H
17 (cont)
2.00
3
DAY 11
11
2001-02-11T08:00 2001-02-11T14:03 11
6H
6
Day 11 Dose 2001-02-11T08:00 PT6H
-PT6H
18 (cont)
3
DAY 11
11
2001-02-11T08:00 2001-02-11T14:03 11
6H
6
Day 11 Dose 2001-02-11T08:00 PT6H
-PT6H
19 (cont)
3
DAY 11
11
2001-02-11T08:00 2001-02-11T14:03 11
6H
6
Day 11 Dose 2001-02-11T08:00 PT6H
-PT6H
20 (cont)
0.10
3
DAY 11
11
2001-02-11T14:00
11
6H
6
Day 11 Dose 2001-02-11T08:00 PT6H
21 (cont)
0.10
3
DAY 11
11
2001-02-11T14:00
11
6H
6
Day 11 Dose 2001-02-11T08:00 PT6H
22 (cont)
2.00
3
DAY 11
11
2001-02-11T14:03 2001-02-11T20:10 11
12H
12
Day 11 Dose 2001-02-11T08:00 PT12H
-PT6H
23 (cont)
2.00
3
DAY 11
11
2001-02-11T14:03 2001-02-11T20:10 11
12H
12
Day 11 Dose 2001-02-11T08:00 PT12H
-PT6H
24 (cont)
3
DAY 11
11
2001-02-11T14:03 2001-02-11T20:10 11
12H
12
Day 11 Dose 2001-02-11T08:00 PT12H
-PT6H
25 (cont)
3
DAY 11
11
2001-02-11T14:03 2001-02-11T20:10 11
12H
12
Day 11 Dose 2001-02-11T08:00 PT12H
-PT6H
26 (cont)
2.00
4
DAY 12
12
2001-02-11T20:03 2001-02-12T08:10 12
24H
24
Day 11 Dose 2001-02-11T08:00 PT24H
-P12H
27 (cont)
2.00
4
DAY 12
12
2001-02-11T20:03 2001-02-12T08:10 12
24H
24
Day 11 Dose 2001-02-11T08:00 PT24H
-P12H
28 (cont)
4
DAY 12
12
2001-02-11T20:03 2001-02-12T08:10 12
24H
24
Day 11 Dose 2001-02-11T08:00 PT24H
-P12H
29 (cont)
4
DAY 12
12
2001-02-11T20:03 2001-02-12T08:10 12
24H
24
Day 11 Dose 2001-02-11T08:00 PT24H
-P12H
30 (cont)
0.10
4
DAY 12
12
2001-02-12T08:00
12
24H
24
Day 11 Dose 2001-02-11T08:00 PT24H
31 (cont)
0.10
4
DAY 12
12
2001-02-12T08:00
12
24H
24
Day 11 Dose 2001-02-11T08:00 PT24H
32 (cont)

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.10.3
1.
2.
3.
4.
5.

ASSUMPTIONS FOR PHARMACOKINETIC PARAMETERS (PP) DOMAIN MODEL

PP Definition: Data describing the parameters of the time-concentration curve for PC data (e.g., area under the curve, Cmax, Tmax).
It is recognized that PP is a derived dataset, and may be produced from an analysis dataset that might have a different structure. As a result, some
sponsors may need to normalize their analysis dataset in order for it to fit into the SDTM-based PP domain.
The structure is one record per PK parameter per time-concentration profile per subject
Information pertaining to all parameters (e.g., number of exponents, model weighting) should be submitted in the SUPPPP dataset.
The following Qualifiers would not generally be used in PP: --BODSYS, --SEV.
6.3.10.4

EXAMPLE FOR PHARMACOKINETIC PARAMETERS (PP) DOMAIN MODEL

Example 1
This example shows PK parameters calculated from time-concentration profiles for parent drug and one metabolite in plasma and urine for one subject on Days 1
(Rows 1-14) and 8 (Rows 15-28). Note that PPRFTDTC is populated in order to link the PP records to the respective PC records. Note that PPSPEC is null for
Clearance records since it is calculated from multiple specimen sources (plasma and urine).
(PP dataset for example 1)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

DOMAIN
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP

PPSEQ
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28

PPGRPID
PPTESTCD
DAY1_PAR
TMAX
DAY1_PAR
CMAX
DAY1_PAR
AUC
DAY1_PAR
THALF_1
DAY1_PAR
THALF_2
DAY1_PAR
VD
DAY1_PAR
CL
DAY1_MET
TMAX
DAY1_MET
CMAX
DAY1_MET
AUC
DAY1_MET
THALF_1
DAY1_MET
THALF_2
DAY1_MET
VD
DAY1_MET
CL
DAY11_PAR
TMAX
DAY11_PAR
CMAX
DAY11_PAR
AUC
DAY11_PAR
THALF_1
DAY11_PAR
THALF_2
DAY11_PAR
VD
DAY11_PAR
CL
DAY11_MET
TMAX
DAY11_MET
CMAX
DAY11_MET
AUC
DAY11_MET THALF_1
DAY11_MET THALF_2
DAY8_MET
VD
DAY8_MET
CL

PPTEST
Time to Max Effect
Max Effect Concentration
Area Under Curve
Half-life of 1st exp phase
Half-life of 2nd exp phase
Vol of Distribution
Clearance
Time to Max Effect
Max Effect Concentration
Area Under Curve
Half-life of 1st exp phase
Half-life of 2nd exp phase
Vol of Distribution
Clearance
Time to Max Effect
Max Effect Concentration
Area Under Curve
Half-life of 1st exp phase
Half-life of 2nd exp phase
Vol of Distribution
Clearance
Time to Max Effect
Max Effect Concentration
Area Under Curve
Half-life of 1st exp phase
Half-life of 2nd exp phase
Vol of Distribution
Clearance

PPCAT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A PARENT
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE
DRUG A METABOLITE

PPORRES
1.87
44.5
294.7
0.75
4.69
10.9
1.68
0.94
22.27
147.35
0.38
2.35
5.45
0.84
1.91
46.0
289.0
0.77
4.50
10.7
1.75
0.96
23.00
144.50
0.39
2.25
5.35
0.88

PPORRESU
h
ug/L
h*mg/L
h
h
L
L/h
h
ug/L
h*mg/L
h
h
L
L/h
h
ug/L
h*mg/L
h
h
L
L/h
h
ug/L
h*mg/L
h
h
L
L/h

Page 179
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
(PP dataset for example 1, continued)
Row
PPSTRESC
1 (cont)
1.87
2 (cont)
44.5
3 (cont)
294.7
4 (cont)
0.75
5 (cont)
4.69
6 (cont)
10.9
7 (cont)
1.68
8 (cont)
0.94
9 (cont)
22.27
10 (cont)
147.35
11 (cont)
0.38
12 (cont)
2.35
13 (cont)
5.45
14 (cont)
0.84
15 (cont)
1.91
16 (cont)
46.0
17 (cont)
289.0
18 (cont)
0.77
19 (cont)
4.50
20 (cont)
10.7
21 (cont)
1.75
22 (cont)
0.96
23 (cont)
23.00
24 (cont)
144.50
25 (cont)
0.39
26 (cont)
2.25
27 (cont)
5.35
28 (cont)
0.88

Page 180
November 12, 2008

PPSTRESN
1.87
44.5
294.7
0.75
4.69
10.9
1.68
0.94
22.27
147.35
0.38
2.35
5.45
0.84
1.91
46.0
289.0
0.77
4.50
10.7
1.75
0.96
23.00
144.50
0.39
2.25
5.35
0.88

PPSTRESU
h
ug/L
h.mg/L
h
h
L
L/h
h
ug/L
h.mg/L
h
h
L
L/h
h
ug/L
h.mg/L
h
h
L
L/h
h
ug/L
h.mg/L
h
h
L
L/h

PPSPEC
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA

VISITNUM
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2

VISIT
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11
DAY 11

PPDTC
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01
2001-03-01

PPRFTDTC
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-01T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00
2001-02-11T08:00

CDISC SDTM Implementation Guide (Version 3.1.2)
6.3.10.5

RELATING PP RECORDS TO PC RECORDS

It is a requirement that sponsors document the concentrations used to calculate each parameter. For many sponsors,
this need is currently met via the analysis metadata. As a result of feedback received from many sponsors on the
draft version of this document, sponsors may continue to document the concentrations used to calculate each
parameter via the analysis datasets.
This section serves as a reference for sponsors who wish to document relationships between PK parameter records in
a Pharmacokinetic Parameter (PP) dataset and specific time-point concentration records in a Pharmacokinetic
Concentration (PC) dataset according to the SDTM using the RELREC table (Section 8.2 and Section 8.3).
129H

6.3.10.5.1

130H

RELATING DATASETS

If all time-point concentrations in PC are used to calculate all parameters for all subjects, then the relationship
between the two datasets can be documented as shown in the table below.
RDOMAIN
PC
PP

USUBJID

IDVAR
PCGRPID
PPGRPID

IDVARVAL

RELTYPE
MANY
MANY

RELID
A
A

Note that incorporating the name of the analyte and the day of the collection into the value of --GRPID (or some
equivalent method for assigning different values of --GRPID for all the combinations of analytes and reference time
points) is necessary when there is more than one reference time point (PCRFTDTC and PPRFTDTC, which are the
same for related records), and more than one analyte (PCTESTCD, copied into PPCAT to indicate the analyte with
which the parameters are associated), since these variables are part of the natural key (see Section 3.2.1.1) for both
datasets. In this case, --GRPID is a surrogate key (Section 3.2.1.1) used for the relationship.
130H

1302H

6.3.10.5.2

RELATING RECORDS

Four possible examples of different types of relationships between PC and PP records for one drug (DRUG X in this
case) are described. For all of these, the actual PC and PP data are the same. The only variables whose values change
across the examples are the sponsor-defined PCGRPID and PPGRPID. As in the case for relating datasets above
(Section 6.3.10.5.1), --GRPID values must take into account all the combinations of analytes and reference time
points, since both are part of the natural key (see Section 3.2.1.1) for both datasets. To conserve space, the PC and
PP domains appear only once, but with four --GRPID columns, one for each of the examples. Note that a submission
dataset would contain only one --GRPID column with a set of values such as those shown in one of the four columns
in the PC and PP datasets, or values defined by the sponsor. Note that --GRPID values in PC and PP do not need to
be the same (e.g., examples show PC with underscores and PP without underscores). The example specifics are as
follows:
130H

1304H

Example 1: All PK time-point-concentration values in the PC dataset are used to calculate all the PK parameters in
the PP dataset for both Days 1 and 8 for one subject.
Pharmacokinetic Concentrations (PC) Dataset For All Examples
Pharmacokinetic Parameters (PP) Dataset For All Examples
RELREC Example 1. All PC records used to calculate all PK parameters
• Method A (Many to many, using PCGRPID and PPGRPID)
• Method B (One to many, using PCSEQ and PPGRPID)
• Method C (Many to one, using PCGRPID and PPSEQ)
• Method D (One to one, using PCSEQ and PPSEQ)
1978H

197H

1980H

198H

1982H

1983H

1984H

Example 2: Two PC values were excluded from the calculation of all PK parameters for the Day 1 data. Day 8
values are related as per Example 1.
RELREC Example 2: Only some records in PC used to calculate all PK parameters
• Method A (Many to many, using PCGRPID and PPGRPID)
• Method B (One to many, using PCSEQ and PPGRPID)
• Method C (Many to one, using PCGRPID and PPSEQ)
• Method D (One to one, using PCSEQ and PPSEQ)
1985H

1986H

1987H

198H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Example 3: Two PC values were excluded from the calculation of two PK parameters, but used in the others for Day 1.
Day 8 values are related as per Example 1.
RELREC Example 3. Only some records in PC used to calculate some parameters
• Method A (Many to many, using PCGRPID and PPGRPID)
• Method B (One to many, using PCSEQ and PPGRPID)
• Method C (Many to one, using PCGRPID and PPSEQ)
• Method D (One to one, using PCSEQ and PPSEQ)
190H

19H

192H

193H

194H

Example 4: Only Some PC records for Day 1 were used to calculate parameters: Time Point 5 was excluded from
Tmax, Time Point 6 from Cmax, and Time Points 11 and 12 were excluded from AUC. Day 8 values are related as
per Example 1.
RELREC Example 4: Only Some records in PC used to calculate parameters
• Method A (Many to many, using PCGRPID and PPGRPID)
• Method B (One to many omitted - see note below)
• Method C (Many to one omitted - see note below)
• Method D (One to one, using PCSEQ and PPSEQ)
195H

196H

197H

For each example, PCGRPID and PPGRPID were used to group related records within each respective dataset. The
values for these, as well as the values for PCSEQ and PPSEQ, were then used to populate combinations of IDVAR
and IDVARVAL in the RELREC table using four methods (A-D) for Examples 1-3. Only two methods (A and D) are
shown for Example 4, due to its complexity. Since the relationship between PC records and PP records for Day 8
data does not change across the examples, it is shown only for Example 1, and not repeated.

Page 182
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Pharmacokinetic Concentrations (PC) Dataset For All Examples
Row STUDYID DOMAIN
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC

PCSEQ PCGRPID
Example 1
1
DY1_DRGX
2
DY1_DRGX
3
DY1_DRGX
4
DY1_DRGX
5
DY1_DRGX
6
DY1_DRGX
7
DY1_DRGX
8
DY1_DRGX
9
DY1_DRGX
10
DY1_DRGX
11
DY1_DRGX
12
DY1_DRGX
13
14
15
16
17
18
19
20
21
22
23
24

PCGRPID
PCGRPID
PCGRPID PCREFID PCTESTCD
Example 2
Example 3
Example 4
DY1_DRGX DY1_DRGX_A DY1_DRGX_A 123-0001-01 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_A 123-0001-02 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_A 123-0001-03 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_A 123-0001-04 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_B 123-0001-05 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_C 123-0001-06 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_A 123-0001-07 DRUG X
EXCLUDE DY1_DRGX_B DY1_DRGX_A 123-0001-08 DRUG X
EXCLUDE DY1_DRGX_B DY1_DRGX_A 123-0001-09 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_A 123-0001-10 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_D 123-0001-11 DRUG X
DY1_DRGX DY1_DRGX_A DY1_DRGX_D 123-0001-12 DRUG X
DY11_DRGX
123-0002-13 DRUG X
DY11_DRGX
123-0002-14 DRUG X
DY11_DRGX
123-0002-15 DRUG X
DY11_DRGX
123-0002-16 DRUG X
DY11_DRGX
123-0002-17 DRUG X
DY11_DRGX
123-0002-18 DRUG X
DY11_DRGX
123-0002-19 DRUG X
DY11_DRGX
123-0002-20 DRUG X
DY11_DRGX
123-0002-21 DRUG X
DY11_DRGX
123-0002-22 DRUG X
DY11_DRGX
123-0002-23 DRUG X
DY11_DRGX
123-0002-24 DRUG X

PCTEST

PCCAT

Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug
Study Drug

ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE
ANALYTE

PCSPEC PCORRES PCORRESU
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA
PLASMA

9
20
31
38
45
47.5
41
35
31
25
18
12
10.0
21.0
32.0
39.0
46.0
48.0
40.0
35.0
30.0
24.0
17.0
11.0

ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

(PC dataset for all example, continued)
Row
1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)
12 (cont)
13 (cont)
14 (cont)
15 (cont)
16 (cont)
17 (cont)
18 (cont)
19 (cont)
20 (cont)
21 (cont)
22 (cont)
23 (cont)
24 (cont)

PCSTRESC PCSTRESN PCSTRESU PCLLOQ VISITNUM
9
20
31
38
45
47.5
41
35
31
25
18
12
10.0
21.0
32.0
39.0
46.0
48.0
40.0
35.0
30.0
24.0
17.0
11.0

9
20
31
38
45
47.5
41
35
31
25
18
12
10.0
21.0
32.0
39.0
46.0
48.0
40.0
35.0
30.0
24.0
17.0
11.0

Page 184
November 12, 2008

ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL
ug/mL

1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00

1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2

VISIT

VISITDY

PCDTC

PCDY PCTPT PCTPTNUM PCTPTREF PCRFTDTC

DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 1
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8
DAY 8

1
1
1
1
1
1
1
1
1
1
1
1
8
8
8
8
8
8
8
8
8
8
8
8

2001-02-01T08:35
2001-02-01T08:55
2001-02-01T09:20
2001-02-01T09:45
2001-02-01T10:10
2001-02-01T10:35
2001-02-01T11:00
2001-02-01T11:50
2001-02-01T12:40
2001-02-01T14:45
2001-02-01T16:50
2001-02-01T18:30
2001-02-08T08:35
2001-02-08T08:55
2001-02-08T09:20
2001-02-08T09:45
2001-02-08T10:10
2001-02-08T10:35
2001-02-08T11:00
2001-02-08T11:50
2001-02-08T12:40
2001-02-08T14:45
2001-02-08T16:50
2001-02-08T18:30

1
1
1
1
1
1
1
1
1
1
1
1
8
8
8
8
8
8
8
8
8
8
8
8

5 min
25 min
50 min
75 min
100 min
125 min
150 min
200 min
250 min
375 min
500 min
600 min
5 min
25 min
50 min
75 min
100 min
125 min
150 min
200 min
250 min
375 min
500 min
600 min

1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
8
9
10
11
12

Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 1 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose
Day 8 Dose

2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-01T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30
2001-02-08T08:30

PCELTM
PT5M
PT25M
PT50M
PT1H15M
PT1H40M
PT2H5M
PT2H30M
PT3H20M
PT4H10M
PT6H15M
PT8H20M
PT10H
PT5M
PT25M
PT50M
PT1H15M
PT1H40M
PT2H5M
PT2H30M
PT3H20M
PT4H10M
PT6H15M
PT8H20M
PT10H

CDISC SDTM Implementation Guide (Version 3.1.2)
Pharmacokinetic Parameters (PP) Dataset For All Examples
Row

STUDYID

DOMAIN

USUBJID

PPSEQ

PPDTC

1
2
3
4
5
6
7
8
9
10
11
12
13
14

ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP
PP

ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

1
2
3
4
5
6
7
8
9
10
11
12
13
14

2001-02-01T08:35
2001-02-01T08:35
2001-02-01T08:35
2001-02-01T08:35
2001-02-01T08:35
2001-02-01T08:35
2001-02-01T08:35
2001-02-08T08:35
2001-02-08T08:35
2001-02-08T08:35
2001-02-08T08:35
2001-02-08T08:35
2001-02-08T08:35
2001-02-08T08:35

Row

PPTESTCD

1 (cont)
2 (cont)
3 (cont)
4 (cont)
5 (cont)
6 (cont)
7 (cont)
8 (cont)
9 (cont)
10 (cont)
11 (cont)
12 (cont)
13 (cont)
14 (cont)

TMAX
CMAX
AUC
T1/2, 1
T1/2, 2
VD
CL
TMAX
CMAX
AUC
T1/2, 1
T1/2, 2
VD
CL

PPTEST
Time to Max Effect
Max Effect Concentration
Area Under Curve
Half-life of 1st exp phase
Half-life of 2nd exp phase
Volume of Distribution
Clearance
Time to Max Effect
Max Effect Concentration
Area Under Curve
Half-life of 1st exp phase
Half-life of 2nd exp phase
Volume of Distribution
Clearance

PPGRPID
Example 1
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX

PPGRPID
Example 2
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX
DY1DRGX

PPGRPID
Example 3
DY1DRGX_A
DY1DRGX_A
DY1DRGX_A
DY1DRGX_HALF
DY1DRGX_HALF
DY1DRGX_A
DY1DRGX_A
DY11DRGX
DY11DRGX
DY11DRGX
DY11DRGX
DY11DRGX
DY11DRGX
DY11DRGX

PPGRPID
Example 4
TMAX
CMAX
AUC
OTHER
OTHER
OTHER
OTHER

PPCAT

PPORRES

PPORRESU

PPSTRESC

PPSTRESN

PPSTRESU

DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X
DRUG X

1.87
44.5
294.7
0.75
4.69
10.9
1.68
1.91
46.0
289.0
0.77
4.50
10.7
1.75

h
ug/L
h*mg/L
h
h
L
L/h
h
ug/L
h*mg/L
h
h
L
L/h

1.87
44.5
294.7
0.75
4.69
10.9
1.68
1.91
46.0
289.0
0.77
4.50
10.7
1.75

h
ug/L
h*mg/L
h
h
L
L/h
h
ug/L
h*mg/L
h
h
L
L/h

Page 185
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
RELREC Example 1. All PC records used to calculate all PK parameters.
Method A (Many to many, using PCGRPID and PPGRPID)
Row
1
2
3
4

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PP
PC
PP

USUBJID
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

IDVAR
PCGRPID
PPGRPID
PCGRPID
PPGRPID

IDVARVAL
DY1_DRGX
DY1DRGX
DY11_DRGX
DY11DRGX

RELTYPE

RELID *
1
1
2
2

* RELID 1 indicates all PC records with PCGRPID = DY1_DRGX are related to all PP records with PPGRPID = DY1DRGX.
* RELID 2 indicates all PC records with PCGRPID = DY8_DRGX are related to all PP records with PPGRPID = DY8DRGX.

Method B (One to many, using PCSEQ and PPGRPID)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPGRPID
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPGRPID

IDVARVAL
1
2
3
4
5
6
7
8
9
10
11
12
DY1DRGX
13
14
15
16
17
18
19
20
21
22
23
24
DY8DRGX

RELTYPE

RELID *
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2

* RELID 1 indicates records with PCSEQ values of 1-12 are related to records with PPGRPID = DY1DRGX.
* RELID 2 indicates records with PCSEQ values of 13-24 are related to records with PPGRPID = DY8DRGX.

Method C (Many to one, using PCGRPID and PPSEQ)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PP
PP
PP
PP
PP
PP
PP
PC
PP
PP
PP
PP
PP
PP
PP

IDVAR
PCGRPID
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PCGRPID
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ

IDVARVAL
DY1_DRGX
1
2
3
4
5
6
7
DY8_DRGX
8
9
10
11
12
13
14

RELTYPE

RELID *
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2

* RELID 1 indicates records with a PCGRPID value of DY1_DRGX are related to records with PPSEQ values of 1-7.
* RELID 2 indicates records with a PCGRPID value of DY8_DRGX are related to records with PPSEQ values of 8-14.
Page 186
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Method D (One to one, using PCSEQ and PPSEQ)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PP
PP
PP
PP
PP
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PP
PP
PP
PP
PP
PP

USUBJID
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ

IDVARVAL
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
7
13
14
15
16
17
18
19
20
21
22
23
24
8
9
10
11
12
13
14

RELTYPE

RELID *
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2

* RELID 1 indicates records with PCSEQ values of 1-12 are related to records with PPSEQ values of 1-7.
* RELID 2 indicates records with PCSEQ values of 13-24 are related to records with PPSEQ values of 8-14.

Page 187
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
RELREC Example 2: Only some records in PC used to calculate all PK parameters: Time Points 8 and 9 on
Day 1 not used for any PK parameters.
Method A (Many to many, using PCGRPID and PPGRPID)
Row
1
2

STUDYID
ABC-123
ABC-123

RDOMAIN
PC
PP

USUBJID
ABC-123-0001
ABC-123-0001

IDVAR
PCGRPID
PPGRPID

IDVARVAL
DY1_DRGX
DY1DRGX

RELTYPE

RELID *
1
1

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates only PC records with PCGRPID = DY1_DRGX are related to all PP records with
PPGRPID = DY1DRGX. PC records with PCGRPID = EXCLUDE were not used.
* RELID 2 indicates all PC records with PCGRPID = DY8_DRGX are related to all PP records with PPGRPID =
DY8DRGX.
Method B (One to many, using PCSEQ and PPGRPID)
Row
1
2
3
4
5
6
7
8
9
10
11

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP

USUBJID
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPGRPID

IDVARVAL
1
2
3
4
5
6
7
10
11
12
DY1DRGX

RELTYPE

RELID *
1
1
1
1
1
1
1
1
1
1
1

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with PCSEQ values of 1-7 and 10-12 are related to records with PPGRPID = DY1DRGX.
* RELID 2 indicates records with PCSEQ values of 13-24 are related to records with PPGRPID = DY8DRGX.
Method C (Many to one, using PCGRPID and PPSEQ)
Row
1
2
3
4
5
6
7
8

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PP
PP
PP
PP
PP
PP
PP

USUBJID
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

IDVAR
PCGRPID
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ

IDVARVAL
DY1_DRGX
1
2
3
4
5
6
7

RELTYPE

RELID *
1
1
1
1
1
1
1
1

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with a PCGRPID value of DY1_DRGX are related to records with PPSEQ values of 1-7.
Method D (One to one, using PCSEQ and PPSEQ)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PP
PP
PP
PP
PP
PP

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ

IDVARVAL
1
2
3
4
5
6
7
10
11
12
1
2
3
4
5
6
7

RELTYPE

RELID *
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with PCSEQ values of 1-7 and 10-12 are related to records with PPSEQ values of 1-7.
Page 188
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
RELREC Example 3. Only some records in PC used to calculate some parameters: Time Points 8 and 9 on
Day 1 not used for half-life calculations, but used for other parameters.
Method A (Many to many, using PCGRPID and PPGRPID)
Row
1
2
3
4
5

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PC
PP
PC
PP

USUBJID
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

IDVAR
PCGRPID
PCGRPID
PPGRPID
PCGRPID
PPGRPID

IDVARVAL
RELTYPE
DY1_DRGX_A
DY1_DRGX_B
DY1DRGX_A
DY1_DRGX_A
DY1DRGX_HALF

RELID *
1
1
1
2
2

The Day 8 relationships are the same as those shown in Example 1.
* RELID of "1" Indicates that all time points on Day 1 (PCGRPID = DY1_DRGX_A and DY1_DRGX_B) were
used to calculate all parameters (PPGRPID = DY1DRGX_A) except half-lives.
* RELID of "2" Indicates only the values for PCGRPID = DY1_DRGX_A were used to calculate the half-lives
(PPGRPID = DY1DRGX_HALF).
Method B (One to many, using PCSEQ and PPGRPID)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPGRPID
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPGRPID

IDVARVAL
RELTYPE
1
2
3
4
5
6
7
8
9
10
11
12
DY1DRGX_A
1
2
3
4
5
6
7
10
11
12
DY1DRGX_HALF

RELID *
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with PCSEQ values of 1-12 are related to records with PPGRPID = DY1DRGX_A
* RELID 2 indicates records with PCSEQ values of 1-7 and 10-12 are related to records with PPGRPID = DY1DRGX_HALF.
Method C (Many to one, using PCGRPID and PPSEQ)
Row
1
2
3
4
5
6
7
8
9
10

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PC
PC
PP
PP
PP
PP
PP
PC
PP
PP

USUBJID
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001
ABC-123-0001

IDVAR
PCGRPID
PCGRPID
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PCGRPID
PPSEQ
PPSEQ

IDVARVAL
DY1_DRGX_A
DY1_DRGX_B
1
2
3
6
7
DY1_DRGX_A
4
5

RELTYPE

RELID *
1
1
1
1
1
1
1
2
2
2

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with a PCGRPID value of DY1_DRGX_A and DY1_DRGX_B are related to records
with PPSEQ values of 1-7.
* RELID 2 indicates records with a PCGRPID value of DAYDY1DRGX_A are related to records with
PPSEQ values of 4 and 5.

Page 189
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

Method D (One to one, using PCSEQ and PPSEQ)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PP
PP
PP
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PP

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PPSEQ

IDVARVAL
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
6
7
1
2
3
4
5
6
7
10
11
12
4
5

RELTYPE

RELID *
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with PCSEQ values of 1-12 are related to records with PPSEQ values of 1-3 and 6-7.
* RELID 2 indicates records with PCSEQ values of 1-7 and 10-12 are related to records with PPSEQ values of 4 and 5.

Page 190
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
RELREC Example 4: Only Some records in PC used to calculate parameters: Time Point 5 excluded from
Tmax, 6 from Cmax, and Time Points 11 and 12 from AUC
Method A (Many to many, using PCGRPID and PPGRPID)
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

STUDYID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

RDOMAIN
PP
PC
PC
PC
PP
PC
PC
PC
PP
PC
PC
PC
PP
PC
PC
PC
PC

IDVAR
PPGRPID
PCGRPID
PCGRPID
PCGRPID
PPGRPID
PCGRPID
PCGRPID
PCGRPID
PPGRPID
PCGRPID
PCGRPID
PCGRPID
PPGRPID
PCGRPID
PCGRPID
PCGRPID
PCGRPID

IDVARVAL
TMAX
DY1DRGX_A
DY1DRGX_C
DY1DRGX_D
CMAX
DY1DRGX_A
DY1DRGX_B
DY1DRGX_D
AUC
DY1DRGX_A
DY1DRGX_B
DY1DRGX_C
OTHER
DY1DRGX_A
DY1DRGX_B
DY1DRGX_C
DY1DRGX_D

RELTYPE

RELID *
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
4

The Day 8 relationships are the same as those shown in Example 1.
* Same RELID of "1" Indicates that Tmax used records with PCGRPID values DY1DRGX_A, DY1DRGX_C, and
DY1DRGX_D.
* Same RELID of "2" Indicates that Cmax used records with PCGRPID values DY1DRGX_A, DY1DRGX_B, and
DY1DRGX_D.
* Same RELID of "3" Indicates that AUC used PCGRPID values DY1DRGX_A, DY1DRGX_B, and
DY1DRGX_C.
* Same RELID of "4" Indicates that all other parameters (PPGRPID = OTHER) used all PC time points: PCGRPID
values DY1DRGX_A, DY1DRGX_B, DY1DRGX_C, and DY1DRGX_D.
Note that in the above RELREC table, the single records in rows 1, 3, 5, 7, and 9, represented by their --GRPIDs
(TMAX, DY1DRGX_C, CMAX, DY1DRGX_B, AUC) could have been referenced by their SEQ values, since both
identify the records sufficiently. At least two other hybrid approaches would have been acceptable as well: using
PPSEQ and PCGRPIDs whenever possible, and using PPGRPID and PCSEQ values whenever possible. Method D
below uses only SEQ values.

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Method D (One to one, using PCSEQ and PPSEQ)
Row

5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51

RDOMAIN
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PC
PP
PP
PP
PP

IDVAR
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PCSEQ
PPSEQ
PPSEQ
PPSEQ
PPSEQ

IDVARVAL
1
2
3
4
6
7
8
9
10
11
12
1
1
2
3
4
5
7
8
9
10
11
12
2
1
2
3
4
5
6
7
8
9
10
3
1
2
3
4
5
6
7
8
9
10
11
12
4
5
6
7

RELTYPE

RELID *
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4

The Day 8 relationships are the same as those shown in Example 1.
* RELID 1 indicates records with PCSEQ values of 1-4 and 6-12 are related to the record with a PPSEQ value of 1.
* RELID 2 indicates records with PCSEQ values of 1-5 and 7-12 are related to the record with a PPSEQ value of 2.
* RELID 3 indicates records with PCSEQ values of 1-10 are related to the record with a PPSEQ value of 3.
* RELID 4 indicates records with PCSEQ values of 1-12 are related to the records with PPSEQ values of 4-7.

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6.3.10.6

Conclusions

Relating the datasets (Section 6.3.10.5.1, and as described in Section 8.3) is the simplest method; however, all timepoint concentrations in PC must be used to calculate all parameters for all subjects. If datasets cannot be related,
then individual subject records must be related (Section 8.2). In either case, the values of PCGRPID and PPGRPID
must take into account multiple analytes and multiple reference time points, if they exist.
1305H

1306H

1307H

Method A, is clearly the most efficient in terms of having the least number of RELREC records, but it does require
the assignment of --GRPID values (which are optional) in both the PC and PP datasets. Method D, in contrast, does
not require the assignment of --GRPID values, instead relying on the required --SEQ values in both datasets to relate
the records. Although Method D results in the largest number of RELREC records compared to the other methods, it
may be the easiest to implement consistently across the range of complexities shown in the examples. Two
additional methods, Methods B and C, are also shown for Examples 1-3. They represent hybrid approaches, using
--GRPID values on only one dataset (PP and PC, respectively) and --SEQ values for the other. These methods are
best suited for sponsors who want to minimize the number of RELREC records while not having to assign --GRPID
values in both domains. Methods B and C would not be ideal, however, if one expected complex scenarios such as
that shown in Example 4.
Please note that an attempt has been made to approximate real PK data; however, the example values are not
intended to reflect data used for actual analysis. When certain time-point concentrations have been omitted from PP
calculations in Examples 2-4, the actual parameter values in the PP dataset have not been recalculated from those in
Example 1 to reflect those omissions.
6.3.10.7

Suggestions for Implementing RELREC in the Submission of PK Data

Determine which of the scenarios best reflects how PP data are related to PC data. Questions that should be
considered:
1. Do all parameters for each PK profile use all concentrations for all subjects? If so, create a PPGRPID value
for all PP records and a PCGRPID value for all PC records for each profile for each subject, analyte, and
reference time point. Decide whether to relate datasets ( Section 6.3.10.5.1) or records (Section 6.3.10.5.2,
Example 1). If choosing the latter, create records in RELREC for each PCGRPID value and each PPGRPID
value (Method A). Use RELID to show which PCGRPID and PPGRPID records are related. Consider
RELREC Methods B, C, and D as applicable.
2. Do all parameters use the same concentrations, although maybe not all of them (Example 2)? If so, create a
single PPGRPID value for all PP records, and two PCGRPID values for the PC records: a PCGRPID value
for ones that were used and a PCGRPID value for those that were not used. Create records in RELREC for
each PCGRPID value and each PPGRPID value (Method A). Use RELID to show which PCGRPID and
PPGRPID records are related. Consider RELREC Methods B, C, and D as applicable.
3. Do any parameters use the same concentrations, but not as consistently as what is shown in Examples 1 and
2? If so, refer to Example 3. Assign a GRPID value to the PP records that use the same concentrations.
More than one PPGRPID value may be necessary. Assign as many PCGRPID values in the PC domain as
needed to group these records. Create records in RELREC for each PCGRPID value and each PPGRPID
value (Method A). Use RELID to show which PCGRPID and PPGRPID records are related. Consider
RELREC Methods B, C, and D as applicable.
4. If none of the above applies, or the data become difficult to group, then start with Example 4, and decide
which RELREC method would be easiest to implement and represent.
1308H

1309H

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6.4 FINDINGS ABOUT EVENTS OR INTERVENTIONS
Findings About Events or Interventions is a specialization of the Findings General Observation Class. As such, it
shares all qualities and conventions of Findings observations but is specialized by the addition of the --OBJ variable.

6.4.1 WHEN TO USE FINDINGS ABOUT
It is intended, as its name implies, to be used when collected data represent ‖findings about‖ an Event or
Intervention that cannot be represented within an Event or Intervention record or as a Supplemental Qualifier to such
a record. Examples include the following:


Data or observations that have different timing from an associated Event or Intervention as a whole:
For example, if severity of an AE is collected at scheduled time points (e.g., per visit) throughout the
duration of the AE, the severities have timing that are different from that of the AE as a whole. Instead, the
collected severities represent ―snapshots‖ of the AE over time.



Data or observations about an Event or Intervention which have Qualifiers of their own that can be represented
in Findings variables (e.g., units, method):
These Qualifiers can be grouped together in the same record to more accurately describe their context and
meaning (rather than being represented by multiple Supplemental Qualifier records). For example, if the
size of a rash is measured, then the result and measurement unit (e.g., centimeters or inches) can be
represented in the Findings About domain in a single record, while other information regarding the rash
(e.g., start and end times), if collected would appear in an Event record.



Data or observations about an Event or Intervention for which no Event or Intervention record has been
collected or created:
For example, if details about a condition (e.g., primary diagnosis) are collected, but the condition was not
collected as Medical History because it was a prerequisite for study participation, then the data can be
represented as results in the Findings About domain, and the condition as the Object of the Observation
(see Section 6.4.3).
130H



Data or information about an Event or Intervention that indicate the occurrence of related symptoms or
therapies:
Depending on the Sponsor‘s definitions of reportable events or interventions and regulatory agreements,
representing occurrence observations in either the Findings About domain or the appropriate Event or
Intervention domain(s) is at the Sponsor‘s discretion. For example, in a migraine study, when symptoms
related to a migraine event are queried and their occurrence is not considered either an AE or a record to be
represented in another Events domain, then the symptoms can be represented in the Findings About
domain.



Data or information that indicate the occurrence of pre-specified AEs:
Since there is a requirement that every record in the AE domain represent an event that actually occurred,
AE probing questions that are answered in the negative (e.g., did not occur, unknown, not done) cannot be
stored in the AE domain. Therefore, answers to probing questions about the occurrence of pre-specified
adverse events can be stored in the Findings About domain, and for each positive response (i.e., where
occurrence indicates yes) there should be a record reflected in the AE domain. The Findings About record
and the AE record may be linked via RELREC.

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6.4.2 NAMING FINDINGS ABOUT DOMAINS
The FA domain is defined to store Findings About Events or Interventions. Sponsors may choose to split the domain
into physically separate datasets following guidance described in Section 4.1.1.7. For example, if Findings About
clinical events and Findings About reproductive events are collected in a study, and are considered separate and
unrelated observations, then they could be split relative to their respective parent domains.
 The DOMAIN value would be ―FA‖
 Variables that require a prefix would use ―FA‖
 Variables of the same name in multiple datasets should have the same SAS Length attribute.
 The dataset names would be the domain name plus up to two additional characters indicating the parent
domain (e.g., FACE for the Findings About clinical events and FARE for findings about reproductive events,
where in this example, ―RE‖ is a custom domain to store reproductive events data).
 FASEQ must be unique within USUBJID for all records across the split datasets.
 Supplemental Qualifier datasets would need to be managed at the split-file level, for example, suppface.xpt
and suppfare.xpt and RDOMAIN would be defined as ―FA‖.
 If a dataset-level RELREC is defined (e.g., between the CE and FACE datasets), then RDOMAIN may
contain up to four characters to effectively describe the relationship between the CE parent records with the
FACE child records.
13H

As described above, if domain splitting is implemented then the dataset name will combine the prefix ―FA‖ with the
two-lettered domain code of the parent record. For example, dataset facm.xpt would store Findings About
Concomitant Medications.

6.4.3 VARIABLES UNIQUE TO FINDINGS ABOUT
The variable, --OBJ, is unique to Findings About. In conjunction with FATESTCD, it describes what the topic of the
observation is; therefore both are required to be populated for every record. FATESTCD describes the
measurement/evaluation and FAOBJ describes the Event or Intervention that the measurement/evaluation is about.
When collected data fit a Qualifier variable listed in SDTM Sections 2.2.1 or 2.2.2, and are represented in the
Findings About domain, then the name of the variable should be used as the value of FATESTCD. For example,
FATESTCD
OCCUR
SEV
TOXGR

FATEST
Occurrence
Severity/Intensity
Toxicity Grade

The use of the same names (e.g., SEV, OCCUR) for both Qualifier variables in the observation classes and
FATESTCD is deliberate, but should not lead users to conclude that the collection of such data (e.g.,
severity/intensity, occurrence) must be stored in the Findings About domain. In fact, data should only be stored in
the Findings About domain if they do not fit in the general-observation-class domain. If the data describe the
underlying Event or Intervention as a whole and share it‘s timing, then the data should be stored as a qualifier of the
general-observation-class record.
In general, the value in FAOBJ should match the value in --TERM or --TRT, unless the parent domain is dictionary
coded or subject to controlled terminology, in which case FAOBJ should then match the value in --DECOD.
Representing collected relationships supporting Findings About data are described in Section 8.6 and are
demonstrated in the examples below (Section 6.4.6).
132H

13H

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6.4.4 FINDINGS ABOUT (FA) DOMAIN MODEL
fa.xpt, Findings About Events or Interventions — Findings Sub-Class, Version 3.1.2. One record per finding, per object, per time point, per visit per
subject Tabulation
Variable
Name
STUDYID
DOMAIN

Variable Label
Study Identifier
Domain Abbreviation

Controlled
Type Terms, Codelist
Role
or Format
Char
Identifier
Char (FA)
Identifier
134H

CDISC Notes

Core

Unique identifier for a study.
Two-character abbreviation for the domain.

Req
Req

References
SDTM 2.2.4
SDTM 2.2.4,
SDTMIG 4.1.2.2,
SDTMIG Appendix
C2
SDTM 2.2.4,
SDTMIG 4.1.2.3
SDTM 2.2.4
135H

136H

USUBJID

Unique Subject Identifier Char

Identifier

FASEQ

Sequence Number

Num

Identifier

FAGRPID

Group ID

Char

Identifier

FASPID

Sponsor-Defined
Identifier

Char

Identifier

Findings About Test
Short Name

Char

FATESTCD

FATEST

Findings About Test
Name

Char

Topic

Synonym
Qualifier

Identifier used to uniquely identify a subject across all studies for all
Req
applications or submissions involving the product.
Sequence Number given to ensure uniqueness of subject records within a Req
domain. May be any valid number.
Used to tie together a block of related records in a single domain for a
Perm
subject.
Sponsor-defined reference number. Perhaps pre-printed on the CRF as an Perm
explicit line identifier or defined in the sponsor‘s operational database.
Example: Line number on a CRF.
Short name of the measurement, test, or examination described in
Req
FATEST. It can be used as a column name when converting a dataset
from a vertical to a horizontal format. The value in FATESTCD cannot
be longer than 8 characters, nor can it start with a number (e.g.
―1TEST‖). FATESTCD cannot contain characters other than letters,
numbers, or underscores. Example: SEV, OCCUR.
Verbatim name of the test or examination used to obtain the
Req
measurement or finding. The value in FATEST cannot be longer than 40
characters. Examples: Severity/Intensity, Occurrence
137H

SDTM 2.2.4,
SDTMIG 4.1.2.6
SDTM 2.2.4,
SDTMIG 4.1.2.6
138H

139H

SDTM 2.2.3,
SDTMIG 4.1.1.8,
SDTMIG 4.1.2.1
1320H

132H

SDTM 2.2.3,
SDTMIG 4.1.2.1,
SDTMIG 4.1.2.4,
SDTMIG 4.1.5.3.1
SDTM 2.2.3.1
132H

1324H

1325H6

FAOBJ

Object of the Observation Char

FACAT

Category for Findings
Char
About
Subcategory for Findings Char
About

FASCAT

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Record
Qualifier

*
*

Grouping
Qualifier
Grouping
Qualifier

Used to describe the object or focal point of the findings observation that Req
is represented by --TEST. Examples: the term (such as Acne) describing
a clinical sign or symptom that is being measured by a Severity test, or
an event such as VOMIT where the volume of Vomit is being measured
by a VOLUME test.
Used to define a category of related records. Examples: GERD,
Perm SDTM 2.2.3,
PRE-SPECIFIED AE.
SDTMIG 4.1.2.6
A further categorization of FACAT.
Perm SDTM 2.2.3,
SDTMIG 4.1.2.6
1327H

1328H

CDISC SDTM Implementation Guide (Version 3.1.2)
Variable
Name
FAORRES

Variable Label
Result or Finding in
Original Units

Controlled
Type Terms, Codelist
Role
or Format
Char
Result
Qualifier

CDISC Notes
Result of the test as originally received or collected.

Core

References

Exp

SDTM 2.2.3,
SDTMIG 4.1.3.6,
SDTMIG 4.1.5.1
Original units in which the data were collected. The unit for FAORRES. Perm SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1,
SDTMIG Appendix
C1
Contains the result value for all findings, copied or derived from
Exp SDTM 2.2.3,
FAORRES in a standard format or standard units. FASTRESC should
SDTMIG 4.1.3.6,
store all results or findings in character format; if results are numeric,
SDTMIG 4.1.5.1
they should also be stored in numeric format in FASTRESN. For
example, if a test has results ―NONE‖, ―NEG‖, and ―NEGATIVE‖ in
FAORRES and these results effectively have the same meaning; they
could be represented in standard format in FASTRESC as
―NEGATIVE‖.
Used for continuous or numeric results or findings in standard format;
Perm SDTM 2.2.3,
copied in numeric format from FASTRESC. FASTRESN should store all
SDTMIG 4.1.5.1
numeric test results or findings.
Standardized unit used for FASTRESC and FASTRESN.
Perm SDTM 2.2.3,
SDTMIG 4.1.3.2,
SDTMIG 4.1.5.1,
SDTMIG Appendix
C1
Used to indicate that the measurement was not done. Should be null if a Perm SDTM 2.2.3,
result exists in FAORRES.
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7,
SDTMIG Appendix
C1
Describes why a question was not answered. Example: subject refused. Perm SDTM 2.2.3,
Used in conjunction with FASTAT when value is NOT DONE.
SDTMIG 4.1.5.1,
SDTMIG 4.1.5.7
Used to specify the location of the clinical evaluation. Example: LEFT Perm SDTM 2.2.3,
ARM
SDTMIG Appendix
C1
Indicator used to identify a baseline value. The value should be ―Y‖ or Perm SDTM 2.2.3,
null.
SDTMIG 4.1.3.7,
SDTMIG Appendix
C1
1329H

130H

FAORRESU Original Units

Char

(UNIT)
13H

Variable
Qualifier

132H

13H

FASTRESC

Character Result/Finding Char
in Std Format

Result
Qualifier

134H

135H

FASTRESN

FASTRESU

Numeric Result/Finding Num
in Standard Units
Standard Units

Char

Result
Qualifier
(UNIT)
137H

Variable
Qualifier

136H

138H

139H

FASTAT

Completion Status

Char

(ND)
198H

Record
Qualifier

1340H

134H

1342H

FAREASND Reason Not Performed

Char

Record
Qualifier

134H

FALOC

Location of the Finding
About

Char

(LOC)

Variable
Qualifier

FABLFL

Baseline Flag

Char

(NY)

Record
Qualifier

1345H

19H

1346H

1347H

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Variable
Name
FAEVAL

VISITNUM

Variable Label
Evaluator

Visit Number

Controlled
Type Terms, Codelist
Role
or Format
Char *
Record
Qualifier

Num

Timing

CDISC Notes

Core

References

Role of the person who provided the evaluation. Used only for results
Perm SDTM 2.2.3,
that are subjective (e.g., assigned by a person or a group). Should be null
SDTMIG 4.1.5.4
for records that contain collected or derived data. Examples:
INVESTIGATOR, ADJUDICATION COMMITTEE, VENDOR.
1348H

1. Clinical encounter number.
2. Numeric version of VISIT, used for sorting.

Exp

1350H

VISIT

Visit Name

Char

Timing

1. Protocol-defined description of clinical encounter.
2. May be used in addition to VISITNUM and/or VISITDY.

Perm

Planned study day of the visit based upon RFSTDTC in Demographics.

Perm

135H

1352H

VISITDY

Planned Study Day of
Visit

Num

Timing

135H

1354H

FADTC

Date/Time of Collection Char

ISO 8601

Timing

Perm
135H

1356H

FADY



Study Day of Collection Num

Timing

1. Study day of collection, measured as integer days.
Perm
2. Algorithm for calculations must be relative to the sponsor-defined
RFSTDTC variable in Demographics. This formula should be consistent
across the submission.
1357H

1358H

Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

6.4.5 ASSUMPTIONS FOR FINDINGS ABOUT DOMAIN MODEL
1. The following qualifiers should generally not be used in FA: --BODSYS, --MODIFY, --SEV, --TOXGR.

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6.4.6 FINDINGS ABOUT EXAMPLES
Example 1: Migraine Symptoms Diary
The form shown below collects severity and symptoms data at multiple time points about a migraine event.
Migraine Symptoms Diary
Migraine Reference Number
When did the migraine start
Answer the following 5 Minutes BEFORE Dosing
Severity of Migraine
Associated Symptoms:
Sensitivity to light
Sensitivity to sound
Nausea
Aura
Answer the following 30 Minutes AFTER Dosing
Severity of Migraine
Associated Symptoms:
Sensitivity to light
Sensitivity to sound
Nausea
Aura
Answer the following 90 Minutes AFTER Dosing
Severity of Migraine
Associated Symptoms:
Sensitivity to light
Sensitivity to sound
Nausea
Aura

xx
DD-MMM-YYYY
HH:MM
○ Mild ○ Moderate ○ Severe
○ No ○ Yes
○ No ○ Yes
○ No ○ Yes
○ No ○ Yes
○ Mild ○ Moderate ○ Severe
○ No ○ Yes
○ No ○ Yes
○ No ○ Yes
○ No ○ Yes
○ Mild ○ Moderate ○ Severe
○ No ○ Yes
○ No ○ Yes
○ No ○ Yes
○ No ○ Yes

The collected data below the migraine start date on the CRF meet the following Findings About criteria: 1) Data that do not describe an Event or Intervention as a
whole and 2) Data that indicate the occurrence of related symptoms.
In this mock scenario, the Sponsor‘s conventions and/or reporting agreements consider migraine as a clinical event (as opposed to a reportable AE) and consider
the pre-specified symptom responses as findings about the migraine, therefore the data are represented in the Findings About domain with FATESTCD =
‖OCCUR‖ and FAOBJ defined as the symptom description. Therefore, the mock datasets represent (1) The migraine event record in the CE domain, (2) The
severity and symptoms data, per time point, in the Findings About domain, and (3) A dataset-level relationship in RELREC based on the sponsor ID (--SPID)
value which was populated with a system generated identifier unique to each iteration of this form.

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ce.xpt
STUDYID
ABC

DOMAIN
CE

USUBJID
ABC-123

CESEQ
1

CESPID
90567

CETERM
Migraine

CEDECOD
Migraine

CESTDTC
2007-05-16T10:30

FATEST
Severity/Intensity
Occurrence
Occurrence
Occurrence
Occurrence
Severity/Intensity
Occurrence
Occurrence
Occurrence
Occurrence
Severity/Intensity
Occurrence
Occurrence
Occurrence
Occurrence

FAOBJ
Migraine
Sensitivity To Light
Sensitivity To Sound
Nausea
Aura
Migraine
Sensitivity To Light
Sensitivity To Sound
Nausea
Aura
Migraine
Sensitivity To Light
Sensitivity To Sound
Nausea
Aura

fa.xpt
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

STUDYID
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC

Row
1 (cont’d)
2 (cont’d)
3 (cont’d)
4 (cont’d)
5 (cont’d)
6 (cont’d)
7 (cont’d)
8 (cont’d)
9 (cont’d)
10 (cont’d)
11 (cont’d)
12 (cont’d)
13 (cont’d)
14 (cont’d)
15 (cont’d)

DOMAIN
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA

FAORRES
SEVERE
Y
N
Y
Y
MODERATE
Y
N
N
Y
MILD
N
N
N
N

USUBJID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
FASTRESC
SEVERE
Y
N
Y
Y
MODERATE
Y
N
N
Y
MILD
N
N
N
N

FASEQ
1
2
3
4
6
7
8
9
10
12
13
14
15
16
18

FASPID
90567
90567
90567
90567
90567
90567
90567
90567
90567
90567
90567
90567
90567
90567
90567

FADTC
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16
2007-05-16

FATESTCD
SEV
OCCUR
OCCUR
OCCUR
OCCUR
SEV
OCCUR
OCCUR
OCCUR
OCCUR
SEV
OCCUR
OCCUR
OCCUR
OCCUR

FATPT
5M PRE-DOSE
5M PRE-DOSE
5M PRE-DOSE
5M PRE-DOSE
5M PRE-DOSE
30M POST-DOSE
30M POST-DOSE
30M POST-DOSE
30M POST-DOSE
30M POST-DOSE
90M POST-DOSE
90M POST-DOSE
90M POST-DOSE
90M POST-DOSE
90M POST-DOSE

FAELTM

-PT5M
-PT5M
-PT5M
-PT5M
-PT5M
PT30M
PT30M
PT30M
PT30M
PT30M
PT90M
PT90M
PT90M
PT90M
PT90M

FACAT
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS
MIGRAINE SYMPTOMS

FATPTREF
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING
DOSING

RELREC
STUDYID
ABC
ABC

RDOMAIN
CE
FA

Page 200
November 12, 2008

USUBJID

IDVAR
CESPID
FASPID

IDVARVAL

RELTYPE
ONE
MANY

RELID
1
1

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 2: Rash Assessment
This CRF collects details about rash events at each visit, until resolved.
Rash Assessment
Date of Assessment
Associated AE reference number
Rash Size
Lesion Type & Count
Macules
Papules
Vesicles
Pustules
Scabs
Scars

DD-MMM-YYYY
________
○0
○0
○0
○0
○0
○0

○ cm

○ 1 to 25
○ 1 to 25
○ 1 to 25
○ 1 to 25
○ 1 to 25
○ 1 to 25

○ in
○ 26 to 100
○ 26 to 100
○ 26 to 100
○ 26 to 100
○ 26 to 100
○ 26 to 100

○ 101 to 200
○ 101 to 200
○ 101 to 200
○ 101 to 200
○ 101 to 200
○ 101 to 200

○ 201 to 300
○ 201 to 300
○ 201 to 300
○ 201 to 300
○ 201 to 300
○ 201 to 300

○ >300
○ >300
○ >300
○ >300
○ >300
○ >300

The collected data meet the following Findings About criteria: 1) Data that do not describe an Event or Intervention as a whole and 2) Data (―about‖ an Event or
Intervention) which have Qualifiers of their own that can be represented in Findings variables (e.g., units, method)
In this mock scenario, the rash event is considered a reportable AE; therefore the form design collects a reference number to the AE form where the event is
captured. Data points collected on the Rash Assessment form can be represented in the Findings About domain and related to the AE via RELREC. Note, in the
mock datasets below, the AE started on May 10, 2007 and the Rash assessment was conducted on May 12 and May 19, 2007. Certain Required or Expected
variables have been omitted in consideration of space and clarity.
ae.xpt
STUDYID

DOMAIN

USUBJID

AESEQ

AESPID

XYZ

XYZ-789

47869

AETERM
Injection
site rash

AEDECOD
Injection site
rash

AEBODSYS
General disorders and
administration site conditions

AELOC
LEFT
ARM

AESEV

AESER

MILD

AEACN
NOT
APPLICABLE

AESTDTC
2007-05-10

fa.xpt
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14

STUDYID
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ

DOMAIN
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA

USUBJID
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789
XYZ-789

FASEQ
123451
123452
123453
123454
123455
123456
123457
123459
123460
123461
123462
123463
123464
123465

FASPID
5
5
5
5
5
5
5
5
5
5
5
5
5
5

FATESTCD
SIZE
COUNT
COUNT
COUNT
COUNT
COUNT
COUNT
SIZE
COUNT
COUNT
COUNT
COUNT
COUNT
COUNT

FATEST
Size
Count
Count
Count
Count
Count
Count
Size
Count
Count
Count
Count
Count
Count

FAOBJ
Injection Site Rash
Macules
Papules
Vesicles
Pustules
Scabs
Scars
Injection Site Rash
Macules
Papules
Vesicles
Pustules
Scabs
Scars

FAORRES
2.5
26 to 100
1 to 25
0
0
0
0
1
1 to 25
1 to 25
0
0
0
0

FAORRESU
IN

VISIT
VISIT 3
VISIT 3
VISIT 3
VISIT 3
VISIT 3
VISIT 3
VISIT 3
VISIT 4
VISIT 4
VISIT 4
VISIT 4
VISIT 4
VISIT 4
VISIT 4

FADTC
2007-05-12
2007-05-12
2007-05-12
2007-05-12
2007-05-12
2007-05-12
2007-05-12
2007-05-19
2007-05-19
2007-05-19
2007-05-19
2007-05-19
2007-05-19
2007-05-19

Page 201
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
RELREC
STUDYID
XYZ
XYZ

RDOMAIN
AE
FA

USUBJID

IDVAR
AESPID
FASPID

IDVARVAL

RELTYPE
ONE
MANY

RELID
23
23

Example 3: Rheumatoid Arthritis History
The form below collects information about rheumatoid arthritis. In this mock scenario, rheumatoid arthritis is a prerequisite for participation in an osteoporosis
trial and was not collected as a Medical History event.
Rheumatoid Arthritis History
Date of Assessment
During the past 6 months, how would you rate the following:
Joint stiffness
Inflammation
Joint swelling
Joint pain (arthralgia)
Malaise
Duration of early morning stiffness (hours and minutes)

DD-MMM-YYYY
○ MILD ○ MODERATE
○ MILD ○ MODERATE
○ MILD ○ MODERATE
○ MILD ○ MODERATE
○ MILD ○ MODERATE
_____Hours _____Minutes

○ SEVERE
○ SEVERE
○ SEVERE
○ SEVERE
○ SEVERE

The collected data meet the following Findings About criteria: Data (―about‖ an Event or Intervention) for which no Event or Intervention record has been
collected or created
In this mock scenario, the rheumatoid arthritis history was assessed on August 13, 2006.
fa.xpt
Row
1
2
3
4
5
6

STUDYID
ABC
ABC
ABC
ABC
ABC
ABC

Row
1 (cont’d)
2 (cont’d)
3 (cont’d)
4 (cont’d)
5 (cont’d)
6 (cont’d)

1
2
3
4
5
6

FATESTCD
SEV
SEV
SEV
SEV
SEV
DUR

FATEST
Severity/Intensity
Severity/Intensity
Severity/Intensity
Severity/Intensity
Severity/Intensity
Duration

FACAT
RHEUMATOID ARTHRITIS HISTORY
RHEUMATOID ARTHRITIS HISTORY
RHEUMATOID ARTHRITIS HISTORY
RHEUMATOID ARTHRITIS HISTORY
RHEUMATOID ARTHRITIS HISTORY
RHEUMATOID ARTHRITIS HISTORY

FAORRES
SEVERE
MODERATE
MODERATE
MODERATE
MILD
PT1H30M

FASTRESC
SEVERE
MODERATE
MODERATE
MODERATE
MILD
PT1H30M

Page 202
November 12, 2008

DOMAIN
FA
FA
FA
FA
FA
FA

USUBJID
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123
ABC-123

FASEQ

FAOBJ
Joint Stiffness
Inflammation
Joint Swelling
Arthralgia
Malaise
Early Morning Stiffness
FADTC
2006-08-13
2006-08-13
2006-08-13
2006-08-13
2006-08-13
2006-08-13

FAEVLINT
-P6M
-P6M
-P6M
-P6M
-P6M
-P6M

CDISC SDTM Implementation Guide (Version 3.1.2)

Example 4: Findings About Fracture Events
In this example, details about bone-fracture events are collected. This form is designed to collect multiple entries of fracture information including an initial entry
for the most recent fracture prior to study participation, as well as entry of information for fractures that occur during the study.
Bone Fracture Assessment
Complete form for most recent fracture prior to study
participation.
Enter Fracture Event Reference Number for all
fractures occurring during study participation:
How did fracture occur

What was the outcome

Additional therapeutic measures required

_____
○ Pathologic
○ Fall
○ Other trauma
○ Unknown
○ Normal Healing
○ Complications
Select all that apply:
□ Complication x
□ Complication y
□ Complication z
○ No
○ Unknown
○ Yes
Select all that apply
□ Therapeutic measure a
□ Therapeutic measure b
□ Therapeutic measure c

The collected data meet the following Findings About criteria: (1) Data (―about‖ an Event or Intervention) that indicate the occurrence of related symptoms or
therapies and (2) Data (―about‖ an event/intervention) for which no Event or Intervention record has been collected or created
Determining when data further describe the parent event record either as Variable Qualifiers or Supplemental Qualifiers may be dependent on data collection
design. In the above form, responses are provided for the most recent fracture but an event record reference number was not collected. But for in-study fracture
events, a reference number is collected which would allow representing the responses as part of the Event record either as Supplemental Qualifiers and/or
variables like --OUT and --CONTRT.
The below domains reflect responses to each Bone Fracture Assessment question. The historical-fracture responses that are without a parent record are
represented in the FA domain, while the current-fracture responses are represented as Event records with Supplemental Qualifiers.
Historical Fractures Having No Event Records

Page 203
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
fa.xpt
STUDYID DOMAIN USUBJID
ABC -USABC
FA
701-002
ABC -USABC
FA
701-002
ABC -USABC
FA
701-002
ABC -USABC
FA
701-002

FASEQ FASPID FATESTCD FATEST

FAOBJ

798654

REAS

Reason

798654

OUT

Outcome

798654

OCCUR

Occurrence

798654

OCCUR

Occurrence

IDVAR
FASEQ
FASEQ
FASEQ
FASEQ

IDVARVAL
1
2
3
4

CESEQ
1
2

CESPID
1
2

FACAT
BONE FRACTURE
Bone Fracture
ASSESSMENT - HISTORY
BONE FRACTURE
Bone Fracture
ASSESSMENT - HISTORY
BONE FRACTURE
Complications
ASSESSMENT
BONE FRACTURE
Therapeutic Measure
ASSESSMENT

FAORRES

FADTC

FALL

2006-04-10

COMPLICATIONS

2006-04-10

suppfa.xpt
STUDYID
ABC
ABC
ABC
ABC

RDOMAIN
FA
FA
FA
FA

USUBJID
ABC -US-701-002
ABC -US-701-002
ABC -US-701-002
ABC -US-701-002

QNAM
FATYP
FATYP
FATYP
FATYP

QLABEL
FA Type
FA Type
FA Type
FA Type

QVAL
MOST RECENT
MOST RECENT
MOST RECENT
MOST RECENT

QORIG
CRF
CRF
CRF
CRF

QEVAL

Current Fractures Having Event Records
ce.xpt
STUDYID
ABC
ABC

DOMAIN
CE
CE

USUBJID
ABC -US-701-002
ABC -US-701-002

RDOMAIN
CE
CE
CE

USUBJID
ABC -US-701-002
ABC -US-701-002
ABC -US-701-002

CETERM
Fracture
Fracture

CELOC
ARM
LEG

CEOUT
NORMAL HEALING
COMPLICATIONS

CECONTRT
Y
N

CESTDTC
2006-07-03
2006-10-15

suppce.xpt
STUDYID
ABC
ABC
ABC

Page 204
November 12, 2008

IDVAR
CESPID
CESPID
CESPID

IDVARVAL
1
2
2

QNAM
REAS
REAS
OUT

QLABEL
Reason
Reason
Outcome

QVAL
FALL
OTHER TRAUMA
COMPLICATIONS

QORIG
CRF
CRF
CRF

QEVAL

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 5: Pre-Specified Adverse Events
In this example, three AEs are pre-specified and are scheduled to be asked at each visit. If the occurrence is yes, then a complete AE record is collected on the AE
form.
Pre-Specified Adverse Events of Clinical Interest
Date of Assessment
DD-MMM-YYYY
Did the following occur? If Yes, then enter a complete record in the AE CRF
Headache
○ No ○ Yes ○ Not Done
Respiratory infection
○ No ○ Yes ○ Not Done
Nausea
○ No ○ Yes ○ Not Done

The collected data meet the following Findings About criteria: Data that indicate the occurrence of pre-specified adverse events.
In this mock scenario, each response to the pre-specified terms is represented in the Findings About domain. For the Y responses, an AE record is represented in
the AE domain with its respective Qualifiers and timing details. In the example below, the AE of ―Headache‖ encompasses multiple pre-specified Y responses
and the AE of ―Nausea‖ asked about on October 10, reported that it occurred and started on October 8 and ended on October 9. Note, in the example below, no
relationship was collected to link the yes responses with the AE entries, therefore no RELREC was created.
fa.xpt
STUDYID DOMAIN USUBJID
QRS
FA
1234

FASEQ FATESTCD FATEST
1
OCCUR
Occurrence

FAOBJ
Headache

FAORRES
Y

FASTRESC FASTAT
Y

FADTC
VISITNUM VISIT
2005-10-01 2
VISIT 2

QRS

1234

OCCUR

Occurrence

Respiratory Infection

QRS

1234

OCCUR

Occurrence

Nausea

QRS

1234

OCCUR

Occurrence

Headache

QRS

1234

OCCUR

Occurrence

Respiratory Infection

QRS

1234

OCCUR

Occurrence

Nausea

2005-10-01 2

VISIT 2

NOT DONE 2005-10-01 2

VISIT 2

2005-10-10 3

VISIT 3

2005-10-10 3

VISIT 3

2005-10-10 3

VISIT 3

ae.xpt
STUDYID DOMAIN USUBJID
QRS
AE
1234
QRS
AE
1234

AESEQ AETERM
1
Headache
2
Nausea

AEDECOD
Headache
Nausea

AEBODSYS
Nervous system disorders
Gastrointestinal disorders

AESEV
MILD
MODERATE

AEACN
NONE
NONE

AEPRESP
Y
Y

AESTDTC
2005-09-30
2005-10-08

AEENDTC
2005-10-09

Page 205
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 6: Findings About GERD
In this example, the following CRF is used to capture data about pre-specified symptoms of the disease under study on a daily basis. The date of the
assessment is captured, but start and end timing of the events are not.
SYMPTOMS

INVESTIGATOR GERD SYMPTOM MEASUREMENT
VOLUME (mL)
NUMBER OF
MAXIMUM SEVERITY
EPISODES
None, Mild, Moderate, Severe

Vomiting
Diarrhea
Nausea

The collected data meet the following Findings About criteria: 1) data that do not describe an Event or Intervention as a whole, and 2) data (―about‖ an Event or
Intervention) having Qualifiers that can be represented in Findings variables (e.g., units, method).
The data below represent data from two visits for one subject. Records occur in blocks of three for Vomit, and in blocks of two for Diarrhea and Nausea.
Rows 1 -3:
Show the results for the Vomiting tests at Visit 1.
Rows 4 and 5:
Show the results for the Diarrhea tests at Visit 1.
Rows 6 and 7:
Show the results for the Nausea tests at Visit 1.
Rows 8-10:
Show the results for the Vomiting tests at Visit 2. These indicate that Vomiting was absent at Visit 2.
Rows 11 and 12: Show the results for the Diarrhea tests at Visit 2.
Rows 13 and 14: Indicate that Nausea was not assessed at Visit 2.
fa.xpt
Row
1
2
3
4
5
6
7
8
9
10
11
12
13
14

STUDYID
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ

Page 206
November 12, 2008

DOMAIN
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA

USUBJID
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002

FASEQ
1
2
3
4
5
6
7
8
9
10
11
12
13
14

FATESTCD
VOL
NUMEPISD
SEV
NUMEPISD
SEV
NUMEPISD
SEV
VOL
NUMEPISD
SEV
NUMEPISD
SEV
NUMEPISD
SEV

FATEST
Volume
Number of Episodes
Severity/Intensity
Number of Episodes
Severity/Intensity
Number of Episodes
Severity/Intensity
Volume
Number of Episodes
Severity/Intensity
Number of Episodes
Severity/Intensity
Number of Episodes
Severity/Intensity

FAOBJ
Vomit
Vomit
Vomit
Diarrhea
Diarrhea
Nausea
Nausea
Vomit
Vomit
Vomit
Diarrhea
Diarrhea
Nausea
Nausea

FACAT
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD

CDISC SDTM Implementation Guide (Version 3.1.2)
Row
1 (cont’d)
2 (cont’d)
3 (cont’d)
4 (cont’d)
5 (cont’d)
6 (cont’d)
7 (cont’d)
8 (cont’d)
9 (cont’d)
10 (cont’d)
11 (cont’d)
12 (cont’d)
13 (cont’d)
14 (cont’d)

FAORRES
250
>10
SEVERE
2
SEVERE
1
MODERATE
0
0
NONE
1
SEVERE

FAORRESU
mL

FASTRESC
250
>10
SEVERE
2
SEVERE
1
MODERATE
0
0
NONE
1
SEVERE

FASTRESU
mL

VISIT
1
1
1
1
1
1
1
2
2
2
2
2
2
2

FASTAT

NOT DONE
NOT DONE

FADTC
2006-02-02
2006-02-02
2006-02-02
2006-02-02
2006-02-02
2006-02-02
2006-02-02
2006-02-03
2006-02-03
2006-02-03
2006-02-03
2006-02-03
2006-02-03
2006-02-03

Example 7: Findings About GERD
This example is similar to the one above except that with the following CRF, which includes a separate column to collect the occurrence of symptoms,
measurements are collected only for symptoms that occurred. There is a record for the occurrence test for each symptom. If Vomiting occurs, there are 3
additional records, and for each occurrence of Diarrhea or Nausea there are two additional records.
Whether there are adverse event records related to these symptoms depends on agreements in place for the study about whether these symptoms are
considered reportable adverse events.
SYMPTOMS
OCCURRED?
Yes/No

INVESTIGATOR GERD SYMPTOM MEASUREMENT (IF SYMPTOM OCCURRED)
VOLUME (mL)
NUMBER OF EPISODES
MAXIMUM SEVERITY
Mild, Moderate, Severe

Vomiting
Diarrhea
Nausea

The collected data meet the following Findings About criteria: 1) data that do not describe an Event or Intervention as a whole, 2) data (―about‖ an
Event or Intervention) having Qualifiers that can be represented in Findings variables (e.g., units, method), and 3) data (―about‖ an Event or
Intervention) that indicate the occurrence of related symptoms or therapies.

Page 207
November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
The data below represent data two visits for one subject.
Rows 1-4: Show the results for the Vomiting tests at Visit 1.
Rows 5-7: Show the results for the Diarrhea tests at Visit 1.
Rows 8-10: Show the results for the Nausea tests at Visit 1.
Row 11:
Show that Vomiting was absent at Visit 2.
Rows 12-14: Show the results for the Diarrhea tests at Visit 2.
Row 15:
Show that Nausea was not assessed at Visit 2.
fa.xpt

Row STUDYID

DOMAIN

USUBJID

FASEQ FATESTCD FATEST

FAOBJ

FACAT FAORRES

1
2
3
4
5
6
7
8
9
10
11
12
13
14

XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ
XYZ

FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA
FA

XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002
XYZ-701-002

XYZ

XYZ-701-002 15

Page 208
November 12, 2008

OCCUR
VOL
NUMEPISD
SEV
OCCUR
NUMEPISD
SEV
OCCUR
NUMEPISD
SEV
OCCUR
OCCUR
NUMEPISD
SEV

Occurrence
Volume
Number of Episodes
Severity/Intensity
Occurrence
Number of Episodes
Severity/Intensity
Occurrence
Number of Episodes
Severity/Intensity
Occurrence
Occurrence
Number of Episodes
Severity/Intensity

Vomit
Vomit
Vomit
Vomit
Diarrhea
Diarrhea
Diarrhea
Nausea
Nausea
Nausea
Vomit
Diarrhea
Diarrhea
Diarrhea

GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD
GERD

OCCUR

Occurrence

Nausea

GERD

Y
250
>10
SEVERE
Y
2
SEVERE
Y
1
MODERATE
N
Y
1
SEVERE

FAORRE
FASTRESC FASTRESU VISIT FASTAT FADTC
SU
Y
1
2006-02-02
mL
250
mL
1
2006-02-02
>10
1
2006-02-02
SEVERE
1
2006-02-02
Y
1
2006-02-02
2
1
2006-02-02
SEVERE
1
2006-02-02
Y
1
2006-02-02
1
1
2006-02-02
MODERATE
1
2006-02-02
N
2
2006-02-03
Y
2
2006-02-03
1
2
2006-02-03
SEVERE
2
2006-02-03
NOT
2
2006-02-03
DONE

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 8: Severity Assessments Per Visit of Adverse Events
The adverse event module collects, instead of a single assessment of severity, assessments of severity at each visit, as follows:
At each visit, record severity of the Adverse Event.
Visit
1
2
3
Severity

The collected data meet the following Findings About criteria: data that do not describe an Event or Intervention as a whole.
AE Domain (For clarity, only selected variables are shown.)
Row 1: Shows the record for a verbatim term of "Morning queasiness", for which the maximum severity over the course of the event was "Moderate."
Row 2: Shows the record for a verbatim term of ―Watery stools‖, for which ―Mild‖ severity was collected at Visits 2 and 3 before the event ended.
ae.xpt
Row

DOMAIN

USUBJID

AESEQ

AETERM

AEDECOD

AESTDTC

AEENDTC

AESEV

123

Morning queasiness

Nausea

2006-02-01

MODERATE

123

Watery stools

Diarrhea

2006-02-01

2006-02-23
2006-02-15

MILD

FA domain
Rows 1-4: Show severity data collected at the four visits that occurred between the start and end of the AE, ―Morning queasiness‖. FAOBJ =
NAUSEA, which is the value of AEDECOD in the associated AE record.
Rows 5-6: Show severity data collected at the two visits that occurred between the start and end of the AE, ―Watery stools.‖ FAOBJ = DIARRHEA,
which is the value of AEDECOD in the associated AE record.
fa.xpt
Row STUDYID
XYZ
1

DOMAIN
FA

USUBJID
XYZ-US-701-002

FASEQ
1

FATESTCD
SEV

FATEST
Severity/Intensity

FAOBJ
Nausea

FAORRES
MILD

VISIT
2

FADTC
2006-02-02

XYZ

XYZ-US-701-002

SEV

Severity/Intensity

Nausea

MODERATE

2006-02-09

XYZ

XYZ-US-701-002

SEV

Severity/Intensity

Nausea

MODERATE

2006-02-16

XYZ

XYZ-US-701-002

SEV

Severity/Intensity

Nausea

MILD

2006-02-23

XYZ

XYZ-US-701-002

SEV

Severity/Intensity

Diarrhea

MILD

2006-02-02

XYZ

XYZ-US-701-002

SEV

Severity/Intensity

Diarrhea

MILD

2006-02-09

RELREC dataset
Depending on how the relationships were collected, in this example, RELREC could be created with either 2 or 6 RELIDs. With 2 RELIDs, the Sponsor
is describing that the severity ratings are related to the AE as well as being related to each other. With 6 RELIDs, the Sponsor is describing that the
severity ratings are related to the AE only (and not to each other).

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Example with two RELIDS
STUDYID
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC

RDOMAIN
AE
FA
FA
FA
FA
AE
FA
FA

USUBJID
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002

IDVAR
AESEQ
FASEQ
FASEQ
FASEQ
FASEQ
AESEQ
FASEQ
FASEQ

IDVARVAL
1
1
2
3
4
2
5
6

RELTYPE

RELID
1
1
1
1
1
2
2
2

USUBJID
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002
XYZ-US-701-002

IDVAR
AESEQ
FASEQ
AESEQ
FASEQ
AESEQ
FASEQ
AESEQ
FASEQ
AESEQ
FASEQ
AESEQ
FASEQ

IDVARVAL
1
1
1
2
1
3
1
4
2
5
2
6

RELTYPE

RELID
1
1
2
2
3
3
4
4
5
5
6
6

Example with six RELIDS
STUDYID
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC
ABC

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RDOMAIN
AE
FA
AE
FA
AE
FA
AE
FA
AE
FA
AE
FA

CDISC SDTM Implementation Guide (Version 3.1.2)

7 Trial Design Datasets
7.1

INTRODUCTION

7.1.1 PURPOSE OF TRIAL DESIGN MODEL
ICH E3, Guidance for Industry, Structure and Content of Clinical Study Reports, Section 9.1, calls for a brief,
clear description of the overall plan and design of the study, and supplies examples of charts and diagrams for this
purpose in Annex IIIa and Annex IIIb. Each Annex corresponds to an example trial, and each shows a diagram
describing the study design and a table showing the schedule of assessments. The Trial Design Model in the
SDTM provides a standardized way to describe those aspects of the planned conduct of a clinical trial shown in
the study design diagrams of these examples. The standard Trial Design Datasets will allow reviewers to:
 clearly and quickly grasp the design of a clinical trial
 compare the designs of different trials
 search a data warehouse for clinical trials with certain features
 compare planned and actual treatments and visits for subjects in a clinical trial.
Modeling a clinical trial in this standardized way requires the explicit statement of certain decision rules that may
not be addressed or may be vague or ambiguous in the usual prose protocol document. Prospective modeling of
the design of a clinical trial should lead to a clearer, better protocol. Retrospective modeling of the design of a
clinical trial should ensure a clear description of how the trial protocol was interpreted by the sponsor.

7.1.2 DEFINITIONS OF TRIAL DESIGN CONCEPTS
A clinical trial is a scientific experiment involving human subjects, which is intended to address certain scientific
questions (the objectives of the trial). [See CDISC glossary for more complete definitions of clinical trial and
objective.]
Trial Design: The design of a clinical trial is a plan for what will be done to subjects, and what data will be
collected about them, in the course of the trial, to address the trial's objectives.
Epoch: As part of the design of a trial, the planned period of subjects' participation in the trial is divided into
Epochs. Each Epoch is a period of time that serves a purpose in the trial as a whole. That purpose will be at the
level of the primary objectives of the trial. Typically, the purpose of an Epoch will be to expose subjects to a
treatment, or to prepare for such a treatment period (e.g., determine subject eligibility, wash out previous
treatments) or to gather data on subjects after a treatment has ended. Note that at this high level a ―treatment‖ is a
treatment strategy, which may be simple (e.g., exposure to a single drug at a single dose) or complex. Complex
treatment strategies could involve tapering through several doses, titrating dose according to clinical criteria,
complex regimens involving multiple drugs, or strategies that involve adding or dropping drugs according to
clinical criteria.
Arm: An Arm is a planned path through the trial. This path covers the entire time of the trial. The group of
subjects assigned to a planned path is also often colloquially called an Arm. The group of subjects assigned to an
Arm is also often called a treatment group, and in this sense, an Arm is equivalent to a treatment group.
Study Cell: Since the trial as a whole is divided into Epochs, each planned path through the trial (i.e., each Arm)
is divided into pieces, one for each Epoch. Each of these pieces is called a Study Cell. Thus, there is a study cell
for each combination of Arm and Epoch. Each Study Cell represents an implementation of the purpose of its
associated Epoch. For an Epoch whose purpose is to expose subjects to treatment, each Study Cell associated
with the Epoch has an associated treatment strategy. For example, a three-Arm parallel trial might have a
Treatment Epoch whose purpose is to expose subjects to one of three study treatments: placebo, investigational
product, or active control. There would be three Study Cells associated with the Treatment Epoch, one for each
Arm. Each of these Study Cells exposes the subject to one of the three study treatments. Another example
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
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involving more complex treatment strategies: a trial compares the effects of cycles of chemotherapy drug A given
alone or in combination with drug B, where drug B is given as a pre-treatment to each cycle of drug A.
Element: An Element is a basic building block in the trial design. It involves administering a planned
intervention, which may be treatment or no treatment, during a period of time. Elements for which the planned
intervention is "no treatment" would include Elements for screening, washout, and follow-up.
Study Cells and Elements: Many, perhaps most, clinical trials, involve a single, simple administration of a
planned intervention within a Study Cell, but for some trials, the treatment strategy associated with a Study Cell
may involve a complex series of administrations of treatment. It may be important to track the component steps
in a treatment strategy both operationally and because secondary objectives and safety analyses require that data
be grouped by the treatment step during which it was collected. The steps within a treatment strategy may involve
different doses of drug, different drugs, or different kinds of care, as in pre-operative, operative, and postoperative periods surrounding surgery. When the treatment strategy for a Study Cell is simple, the Study Cell will
contain a single Element, and for many purposes there is little value in distinguishing between the Study Cell and
the Element. However, when the treatment strategy for a Study Cell consists of a complex series of treatments, a
Study Cell can contain multiple Elements. There may be a fixed sequence of Elements, or a repeating cycle of
Elements, or some other complex pattern. In these cases, the distinction between a Study Cell and an Element is
very useful.
Branch: In a trial with multiple Arms, the protocol plans for each subject to be assigned to one Arm. The time
within the trial at which this assignment takes place is the point at which the Arm paths of the trial diverge, and
so is called a branch point. For many trials, the assignment to an Arm happens all at one time, so the trial has one
branch point. For other trials, there may be two or more branches that collectively assign a subject to an Arm. The
process that makes this assignment may be a randomization, but it need not be.
Treatments: The word "treatment" may be used in connection with Epochs, Study Cells, or Elements, but has
somewhat different meanings in each context:
 Since Epochs cut across Arms, an "Epoch treatment" is at a high level that does not specify anything that
differs between Arms. For example, in a three-period crossover study of three doses of Drug X, each
treatment Epoch is associated with Drug X, but not with a specific dose.
 A "Study Cell treatment" is specific to a particular Arm. For example, a parallel trial might have Study
Cell treatments Placebo and Drug X, without any additional detail (e.g., dose, frequency, route of
administration) being specified. A Study Cell treatment is at a relatively high level, the level at which
treatments might be planned in an early conceptual draft of the trial, or in the title or objectives of the
trial.
 An ―Element treatment‖ may be fairly detailed. For example, for an Element representing a cycle of
chemotherapy, Element treatment might specify 5 daily 100 mg doses of Drug X.
The distinctions between these levels are not rigid, and depend on the objectives of the trial. For example, route is
generally a detail of dosing, but in a bioequivalence trial that compared IV and oral administration of Drug X,
route is clearly part of Study Cell treatment.
Visit: A clinical encounter. The notion of a Visit derives from trials with outpatients, where subjects interact with
the investigator during Visits to the investigator's clinical site. However, the term is used in other trials, where a
trial Visit may not correspond to a physical Visit. For example, in a trial with inpatients, time may be subdivided
into Visits, even though subjects are in hospital throughout the trial. For example, data for a screening Visit may
be collected over the course of more than one physical visit. One of the main purposes of Visits is the
performance of assessments, but not all assessments need take place at clinic Visits; some assessments may be
performed by means of telephone contacts, electronic devices or call-in systems. The protocol should specify
what contacts are considered Visits and how they are defined.

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7.1.3 CURRENT AND FUTURE CONTENTS OF THE TRIAL DESIGN MODEL
The datasets currently included in the Trial Design Model:
 Trial Arms: describes the sequences of Elements in each Epoch for each Arm, and thus describes the
complete sequence of Elements in each Arm.
 Trial Elements: describes the Elements used in the trial.
 Trial Visits: describes the planned schedule of Visits.
 Trial Inclusion/Exclusion: describes the inclusion/exclusion criteria used to screen subjects.
 Trial Summary: lists key facts (parameters) about the trial that are likely to appear in a registry of clinical
trials.
The Trial Inclusion/Exclusion (TI) is discussed in Section 7.5. The IE domain (subject specific inclusion/exclusion
criteria not met) described in Section 6.3.2 contains the actual exceptions to those criteria for enrolled subjects. The
Trial Inclusion/Exclusion dataset was developed before the define.xml standard for metadata. Because the text of all
inclusion/exclusion criteria can now be included in define.xml, this dataset may be deprecated in future versions of
the SDTM.
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1360H

Future versions of the Trial Design Model are expected to include additional aspects of clinical trials; some of these
additional aspects will be used in submissions, while others are needed for accurate representation of protocols for
the planning stage, but will have limited effects on the SDTM.
Work is underway on representing the schedule of assessments and planned interventions. When this work is
completed, it is expected that the information on planned assessments and interventions will be submitted along with
SDTM datasets containing actual subject data, to allow the comparison of planned and actual assessments and
interventions.
The current Trial Design Model has limitations in representing protocols, which include the following:
 plans for indefinite numbers of repeating Elements (e.g., indefinite numbers of chemotherapy cycles)

indefinite numbers of Visits (e.g., periodic follow-up Visits for survival)

indefinite numbers of Epochs
 indefinite numbers of Arms.
The last two situations arise in dose-escalation studies where increasing doses are given until stopping criteria are
met. Some dose-escalation studies enroll a new cohort of subjects for each new dose, and so, at the planning stage,
have an indefinite number of Arms. Other dose-escalation studies give new doses to a continuing group of subjects,
and so are planned with an indefinite number of Epochs.
There may also be limitations in representing other patterns of Elements within a Study Cell that are more complex
than a simple sequence. For the purpose of submissions about trials that have already completed, these limitations
are not critical, so it is expected that development of the Trial Design Model to address these limitations will have a
minimal impact on SDTM.

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7.2 TRIAL ARMS
This section contains:
 The Trial Arms dataset and assumptions
 A series of example trials, which illustrate the development of the Trial Arms dataset
 Advice on various issues in the development of the Trial Arms dataset
 A recap of the Trial Arms dataset, and the function of its variables.

7.2.1 TRIAL ARMS DATASET — TA
ta.xpt, Trial Arms — Trial Design, Version 3.1.2. One record per planned Element per Arm
Controlled
Terms,
Variable Label Type
Role
CDISC Notes
Core
Codelist or
Format
STUDYID
Study Identifier
Char
Identifier Unique identifier for a study.
Req
DOMAIN
Domain Abbreviation Char TA
Identifier Two-character abbreviation for the domain.
Req
ARMCD
Planned Arm Code Char *
Topic
ARMCD is limited to 20 characters and does not have
Req
special character restrictions. The maximum length of
ARMCD is longer than that for other ―short‖ variables to
accommodate the kind of values that are likely to be needed
for crossover trials. For example, if ARMCD values for a
seven-period crossover were constructed using two-character
abbreviations for each treatment and separating hyphens, the
length of ARMCD values would be 20.
ARM
Description of
Char *
Synonym Name given to an Arm or treatment group.
Req
Planned Arm
Qualifier
TAETORD Order of Element
Num
Identifier Number that gives the order of the Element within the Arm. Req
within Arm
ETCD
Element Code
Char *
Record
ETCD (the companion to ELEMENT) is limited to 8
Req
Qualifier characters and does not have special character restrictions.
These values should be short for ease of use in
programming, but it is not expected that ETCD will need to
serve as a variable name.
ELEMENT Description of
Char *
Synonym The name of the Element. The same Element may occur Perm
Element
Qualifier more than once within an Arm.
TABRANCH Branch
Char
Rule
Condition subject met, at a ―branch‖ in the trial design at the Exp
end of this Element, to be included in this Arm; (e.g.,
randomization to DRUG X).
TATRANS Transition Rule
Char
Rule
If the trial design allows a subject to transition to an Element Exp
other than the next Element in sequence, then the conditions
for transitioning to those other Elements, and the alternative
Element sequences, are specified in this rule (e.g.,
Responders go to washout).
EPOCH
Epoch
Char *
Timing
Name of the Trial Epoch with which this Element of the
Req
Arm is associated.
Variable
Name

136H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

7.2.2 ASSUMPTIONS FOR TA DATASET
1.

TAETORD is an integer. In general the value of TAETORD is 1 for the first Element in each Arm, 2 for the second
Element in each Arm, etc. Occasionally, it may be convenient to skip some values (see Section 7.2.3.6 for an example).
Although the values of TAETORD need not always be sequential, their order must always be the correct order for the
Elements in the Arm path.
Elements in different Arms with the same value of TAETORD may or may not be at the same time, depending on the
design of the trial. The example trials illustrate a variety of possible situations. The same Element may occur more than
once within an Arm.
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TABRANCH describes the outcome of a branch decision point in the trial design for subjects in the Arm. A branch
decision point takes place between Epochs, and is associated with the Element that ends at the decision point. For
instance, if subjects are assigned to an Arm where they receive treatment A through a randomization at the end of Element
X, the value of TABRANCH for Element X would be "Randomized to A."
4. Branch decision points may be based on decision processes other than randomizations, such as clinical evaluations of
disease response or subject choice.
5. There is usually some gap in time between the performance of a randomization and the start of randomized treatment.
However, in many trials this gap in time is small and it is highly unlikely that subjects will leave the trial between
randomization and treatment. In these circumstances, the trial does not need to be modeled with this time period between
randomization and start of treatment as a separate Element.
6. Some trials include multiple paths that are closely enough related so that they are all considered to belong to one Arm. In
general, this set of paths will include a "complete" path along with shorter paths that skip some Elements. The sequence of
Elements represented in the Trial Arms should be the complete, longest path. TATRANS describes the decision points that
may lead to a shortened path within the Arm.
7. If an Element does not end with a decision that could lead to a shortened path within the Arm, then TATRANS will be
blank. If there is such a decision, TATRANS will be in a form like, "If condition X is true, then go to Epoch Y" or "If
condition X is true, then go to Element with TAETORD=Z."
8. EPOCH is not strictly necessary for describing the sequence of Elements in an Arm path, but it is the conceptual basis for
comparisons between Arms, and also provides a useful way to talk about what is happening in a blinded trial while it is
blinded. During periods of blinded treatment, blinded participants will not know which Arm and Element a subject is in,
but EPOCH should provide a description of the time period that does not depend on knowing Arm.
9. EPOCH should be assigned in such a way that Elements from different Arms with the same value of EPOCH are
"comparable" in some sense. The degree of similarity across Arms varies considerably in different trials, as illustrated in
the examples.
10. Note that Study Cells are not explicitly defined in the Trial Arms dataset. A set of records with a common value of both
ARMCD and EPOCH constitute the description of a Study Cell. Transition rules within this set of records are also part of
the description of the Study Cell.
11. EPOCH may be used as a timing variable in other datasets, such as EX and DS, and values of EPOCH must be different
for different epochs. For instance, in a crossover trial with three treatment epochs, each must be given a distinct name; all
three cannot be called ―TREATMENT‖.

7.2.3 TRIAL ARMS EXAMPLES
The core of the Trial Design Model is the Trial Arms (TA) dataset. For each Arm of the trial, it contains one record
for each occurrence of an Element in the path of the Arm.
Although the Trial Arms dataset has one record for each trial Element traversed by subjects assigned to the Arm, it is
generally more useful to work out the overall design of the trial at the Study Cell level, then to work out the
Elements within each Study Cell, and finally to develop the definitions of the Elements that are contained in the
Trial Elements table.
It is generally useful to draw diagrams, like those mentioned in ICH E3, when working out the design of a trial. The
protocol may include a diagram that can serve as a starting point. Such a diagram can then be converted into a Trial
Design Matrix, which displays the Study Cells and which can be, in turn, converted into the Trial Arms dataset.
This section uses example trials of increasing complexity, numbered 1 to 7, to illustrate the concepts of trial design. For each
example trial, the process of working out the Trial Arms table is illustrated by means of a series of diagrams and tables,
including the following:
 A diagram showing the branching structure of the trial in a ―study schema‖ format such as might appear in
a protocol.
 A diagram that shows the ―prospective‖ view of the trial, the view of those participating in the trial. It is
similar to the "study schema" view in that it usually shows a single pool of subjects at the beginning of the
trial, with the pool of subjects being split into separate treatment groups at randomizations and other
branches. They show the epochs of the trial, and, for each group of subjects and each epoch, the sequence
of elements within each epoch for that treatment group. The arms are also indicated on these diagrams.

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





A diagram that shows the ―retrospective‖ view of the trial, the view of the analyst reporting on the trial. The
style of diagram looks more like a matrix; it is also more like the structure of the Trial Arms dataset. It is an
arm-centered view, which shows, for each study cell (epoch/arm combination) the sequence of elements
within that study cell. It can be thought of as showing, for each arm the elements traversed by a subject
who completed that arm as intended.
If the trial is blinded, a diagram that shows the trial as it appears to a blinded participant.
A Trial Design Matrix, an alternative format for representing most of the information in the diagram that
shows Arms and Epochs, and emphasizes the Study Cells.
The Trial Arms dataset.

Readers are advised to read the following section with Example 1 before reading other examples, since Example 1
explains the conventions used for the diagrams and tables.
7.2.3.1 EXAMPLE TRIAL 1, A PARALLEL TRIAL
Diagrams that represent study schemas generally conceive of time as moving from left to right, use horizontal lines
to represent periods of time, and slanting lines to represent branches into separate treatments, convergence into a
common follow-up, or cross-over to a different treatment.
In this document, diagrams are drawn using "blocks" corresponding to trial Elements rather than horizontal lines.
Trial Elements are the various treatment and non-treatment time periods of the trial, and we want to emphasize the
separate trial Elements that might otherwise be "hidden" in a single horizontal line. See Section 7.3 for more
information about defining trial Elements. In general, the Elements of a trial will be fairly clear. However, in the
process of working out a trial design, alternative definitions of trial Elements may be considered, in which case
diagrams for each alternative may be constructed.
136H

In the study schema diagrams in this document, the only slanting lines used are those that represent branches, the
decision points where subjects are divided into separate treatment groups. One advantage of this style of diagram,
which does not show convergence of separate paths into a single block, is that the number of Arms in the trial can be
determined by counting the number of parallel paths at the right end of the diagram.
Below is the study schema diagram for Example Trial 1, a simple parallel trial. This trial has three Arms, corresponding
to the three possible left-to-right "paths" through the trial. Each path corresponds to one of the three treatment Elements
at the right end of the diagram. Note that the randomization is represented by the three red arrows leading from the
Run-in block.

Example Trial 1: Parallel Design
Study schema
Placebo
Screen

Run-In

Drug A
Drug B

Randomization

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The next diagram for this trial shows the epochs of the trial, indicates the three Arms, and shows the sequence of
elements for each group of subjects in each epoch. The arrows are at the right hand side of the diagram because it is
at the end of the trial that all the separate paths through the trial can be seen. Note that, in this diagram, the
randomization, which was shown using three red arrows connecting the Run-in block with the three treatment blocks
in the first diagram, is now indicated by a note with an arrow pointing to the line between two epochs.

Example Trial 1: Parallel Design
Prospective view
Screening
Epoch

Screen

Run-in
Epoch

Run-In

Treatment
Epoch
Placebo

Placebo

Drug A

Drug B

Randomization

The next diagram can be thought of as the ―retrospective‖ view of a trial, the view back from a point in time when a
subject‘s assignment to an arm is known. In this view, the trial appears as a grid, with an arm represented by a series
of study cells, one for each epoch, and a sequence of elements within each study cell. In this trial, as in many trials,
there is exactly one element in each study cell, but later examples will illustrate that this is not always the case.

Example Trial 1: Parallel Design
Retrospective view
Screening
Epoch

Run-in
Epoch

Treatment
Epoch

Screen

Run-In

Placebo

Screen

Run-In

Drug A

Screen

Run-In

Drug B

Randomization

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The next diagram shows the trial from the viewpoint of blinded participants. To blinded participants in this trial, all
Arms look alike. They know when a subject is in the Screen Element, or the Run-in Element, but when a subject is
in the Treatment Epoch, they know only that the subject is in an Element which involves receiving study drug, not
which study drug, and therefore not which Element.

Example Trial 1: Parallel Trial
Blinded View
Screening
Epoch

Run-in
Epoch

Treatment
Epoch

Screen

Run-In

Study Drug

Blind

Randomization

A trial design matrix is a table with a row for each Arm in the trial and a column for each Epoch in the trial. It is closely
related to the retrospective view of the trial, and many users may find it easier to construct a table than to draw a
diagram. The cells in the matrix represent the Study Cells, which are populated with trial Elements. In this trial, each
Study Cell contains exactly one Element.
The columns of a Trial Design Matrix are the Epochs of the trial, the rows are the Arms of the trial, and the cells of
the matrix (the Study Cells) contain Elements. Note that the randomization is not represented in the Trial Design
Matrix. All the diagrams above and the trial design matrix below are alternative representations of the trial design.
None of them contains all the information that will be in the finished Trial Arms dataset, but users may find it useful
to draw some or all of them when working out the dataset.
Trial Design Matrix for Example Trial 1
Screen
Screen
Placebo
Screen
A
Screen
B

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Run-in
Run-in
Run-in
Run-in

Treatment
PLACEBO
DRUG A
DRUG B

CDISC SDTM Implementation Guide (Version 3.1.2)

For Example Trial 1, the conversion of the Trial Design Matrix into the Trial Arms dataset is straightforward. For
each cell of the matrix, there is a record in the Trial Arms dataset. ARM, EPOCH, and ELEMENT can be populated
directly from the matrix. TAETORD acts as a sequence number for the Elements within an Arm, so it can be
populated by counting across the cells in the matrix. The randomization information, which is not represented in the
Trial Design Matrix, is held in TABRANCH in the Trial Arms dataset. TABRANCH is populated only if there is a
branch at the end of an Element for the Arm. When TABRANCH is populated, it describes how the decision at the
branch point would result in a subject being in this Arm.
Trial Arms Dataset for Example Trial 1
Row
1
2

STUDYID
EX1
EX1

DOMAIN
TA
TA

ARMCD
P
P

ARM
Placebo
Placebo

TAETORD
1
2

ETCD
SCRN
RI

ELEMENT
Screen
Run-In

3
4
5

EX1
EX1
EX1

TA
TA
TA

P
A
A

Placebo
A
A

3
1
2

P
SCRN
RI

Placebo
Screen
Run-In

6
7
8

EX1
EX1
EX1

TA
TA
TA

A
B
B

3
1
2

A
SCRN
RI

Drug A
Screen
Run-In

EX1

Drug B

TABRANCH

TATRANS

Randomized
to Placebo

Randomized
to Drug A

Randomized
to Drug B

Treatment
Screen
Run-In
Treatment
Screen
Run-In
Treatment

7.2.3.2 EXAMPLE TRIAL 2, A CROSSOVER TRIAL
The diagram below is for a crossover trial. However, the diagram does not use the crossing slanted lines sometimes
used to represent crossover trials, since the order of the blocks is sufficient to represent the design of the trial.
Slanted lines are used only to represent the branch point at randomization, when a subject is assigned to a sequence
of treatments. As in most crossover trials, the Arms are distinguished by the order of treatments, with the same
treatments present in each Arm. Note that even though all three Arms of this trial end with the same block, the block
for the follow-up Element, the diagram does not show the Arms converging into one block. Also note that the same
block (the "Rest" Element) occurs twice within each Arm. Elements are conceived of as "reusable" and can appear
in more than one Arm, in more than one Epoch, and more than once in an Arm.

EPOCH
Screen
Run-In

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The next diagram for this crossover trial shows the prospective view of the trial, identifies the epoch and
arms of the trial, and gives each a name. As for most crossover studies, the objectives of the trial will be
addressed by comparisons between the arms and by within-subject comparisons between treatments. The
design thus depends on differentiating the periods during which the subject receives the three different
treatments and so there are three different treatment epochs. The fact that the rest periods are identified as
separate Epochs suggests that these also play an important part in the design of the trial; they are probably
designed to allow subjects to return to ―baseline‖ with data collected to show that this occurred. Note that
Epochs are not considered "reusable", so each Epoch has a different name, even though all the Treatment
Epochs are similar and both the Rest Epochs are similar. As with the first example trial, there is a one to
one relationship between the Epochs of the trial and the Elements in each Arm.

The next diagram shows the retrospective view of the trial.

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The last diagram for this trial shows the trial from the viewpoint of blinded participants. As in the simple
parallel trial above, blinded participants see only one sequence of Elements, since during the treatment Epochs
they do not know which of the treatment Elements a subject is in.

Example Trial 2: Crossover Trial
Blinded View
Screen
Epoch

First
Treatment
Epoch

Screen

Drug

First
Second Second
Third
Follow-up
Rest
Treatment
Rest
Treatment
Epoch
Epoch
Epoch
Epoch
Epoch

Rest

Drug

Rest

Drug

Blind

Randomization

The trial design matrix for the crossover example trial is shown below. It corresponds closely to the retrospective
diagram above.
Trial Design Matrix for Example Trial 2
Screen
P-5-10
5-P-10
5-10-P

Screen
Screen
Screen

First
Treatment
Placebo
5 mg
5 mg

First Rest
Rest
Rest
Rest

Second
Treatment
5 mg
Placebo
10 mg

Second Rest
Rest
Rest
Rest

Third
Treatment
10 mg
10 mg
Placebo

Follow-up
Follow-up
Follow-up
Follow-up

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It is straightforward to produce the Trial Arms dataset for this crossover trial from the diagram showing Arms and Epochs, or from the Trial Design Matrix. To
avoid confusion between the ―Screen‖ Epoch, and the ―Screen‖ Element, the word ―Epoch‖ has been included in all the Epoch names.
Trial Arms Dataset for Example Trial 2
Row

STUDYID

DOMAIN

ARMCD

ARM

TAETORD

ETCD

ELEMENT

TABRANCH

1
2
3
4

EX2
EX2
EX2
EX2

TA
TA
TA
TA

P-5-10
P-5-10
P-5-10
P-5-10

Placebo-5mg-10mg
Placebo-5mg-10mg
Placebo-5mg-10mg
Placebo-5mg-10mg

1
2
3
4

SCRN
P
REST
5

Screen
Placebo
Rest
5 mg

Randomized to Placebo - 5 mg - 10 mg

5
6
7
8
9
10
11

EX2
EX2
EX2
EX2
EX2
EX2
EX2

TA
TA
TA
TA
TA
TA
TA

P-5-10
P-5-10
P-5-10
5-P-10
5-P-10
5-P-10
5-P-10

Placebo-5mg-10mg
Placebo-5mg-10mg
Placebo-5mg-10mg
5mg-Placebo-10mg
5mg-Placebo-10mg
5mg-Placebo-10mg
5mg-Placebo-10mg

5
6
7
1
2
3
4

REST
10
FU
SCRN
5
REST
P

Rest
10 mg
Follow-up
Screen
5 mg
Rest
Placebo

Randomized to 5 mg - Placebo - 10 mg

12
13
14
15
16
17
18

EX2
EX2
EX2
EX2
EX2
EX2
EX2

TA
TA
TA
TA
TA
TA
TA

5-P-10
5-P-10
5-P-10
5-10-P
5-10-P
5-10-P
5-10-P

5mg-Placebo-10mg
5mg-Placebo-10mg
5mg-Placebo-10mg
5mg-10mg-Placebo
5mg-10mg-Placebo
5mg-10mg-Placebo
5mg-10mg-Placebo

5
6
7
1
2
3
4

REST
10
FU
SCRN
5
REST
10

Rest
10 mg
Follow-up
Screen
5 mg
Rest
10 mg

Randomized to 5 mg - 10 mg – Placebo

19
20
21

EX2
EX2
EX2

TA
TA
TA

5-10-P
5-10-P
5-10-P

5mg-10mg-Placebo
5mg-10mg-Placebo
5mg-10mg-Placebo

5
6
7

REST
P
FU

Rest
Placebo
Follow-up

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TATRANS

EPOCH

Screen Epoch
First Treatment Epoch
First Rest Epoch
Second Treatment
Epoch
Second Rest Epoch
Third Treatment Epoch
Follow-up Epoch
Screen Epoch
First Treatment Epoch
First Rest Epoch
Second Treatment
Epoch
Second Rest Epoch
Third Treatment Epoch
Follow-up Epoch
Screen Epoch
First Treatment Epoch
First Rest Epoch
Second Treatment
Epoch
Second Rest Epoch
Third Treatment Epoch
Follow-up Epoch

CDISC SDTM Implementation Guide (Version 3.1.2)

7.2.3.3 EXAMPLE TRIAL 3, A TRIAL WITH MULTIPLE BRANCH POINTS
Each of the paths for the trial shown in the diagram below goes through one branch point at randomization, and then
through another branch point when response is evaluated. This results in four Arms, corresponding to the number of
possible paths through the trial, and also to the number of blocks at the right end of the diagram. The fact that there
are only two kinds of block at the right end ("Open DRUG X" and "Rescue") does not affect the fact that there are
four "paths" and thus four Arms.

Example Trial 3: Multiple Branches
Study Schema
Open A
Drug A
Rescue
Screen
Open A
Drug B
Rescue
Randomization
Response Evaluation

The next diagram for this trial is the prospective view. It shows the epochs of the trial and how the initial group of
subjects is split into two treatment groups for the double blind treatment epoch, and how each of those initial treatment
groups is split in two at the response evaluation, resulting in the four Arms of this trial The names of the Arms have
been chosen to represent the outcomes of the successive branches that, together, assign subjects to Arms. These
compound names were chosen to facilitate description of subjects who may drop out of the trial after the first branch
and before the second branch. See Example 7 in Section 5.1.1.2, which illustrates DM and SE data for such subjects.
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Example Trial 3 : Multiple Branches
Prospective View
Screen Double Blind
Open
Epoch Treatment Treatment
Epoch
Epoch
Open A

A-Open

Rescue

A-Rescue

Open A

B-Open

Rescue

B-Rescue

Drug A
Screen
Drug B

Randomization
Response Evaluation

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The next diagram shows the retrospective view. As with the first two example trials, there is one element in each study cell.

Example Trial 3 : Multiple Branches
Retrospective View
Screen Double Blind
Open
Epoch Treatment Treatment
Epoch
Epoch
Screen

Drug A

Open A

A-Open

Screen

Drug A

Rescue

A-Rescue

Screen

Drug B

Open A

B-Open

Screen

Drug B

Rescue

B-Rescue

Randomization
Response Evaluation

The last diagram for this trial shows the trial from the viewpoint of blinded participants. Since the prospective view is the
view most relevant to study participants, the blinded view shown here is a prospective view. Since blinded participants
can tell which treatment a subject receives in the Open Label Epoch, they see two possible element sequences.

Example Trial 3 : Multiple Branches
Blinded Prospective View
Screen Double Blind
Open
Epoch Treatment Treatment
Epoch
Epoch

Screen

Open A

Drug-Open A

Rescue

Drug-Rescue

Drug

Randomization
Response Evaluation

The trial design matrix for this trial can be constructed easily from the diagram showing Arms and Epochs.
Trial Design Matrix for Example Trial 3
A-Open A
A-Rescue
B-Open A
B-Rescue

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Screen
Screen
Screen
Screen
Screen

Double Blind
Treatment A
Treatment A
Treatment B
Treatment B

Open Label
Open Drug A
Rescue
Open Drug A
Rescue

CDISC SDTM Implementation Guide (Version 3.1.2)

Creating the Trial Arms dataset for Example Trial 3 is similarly straightforward. Note that because there are two branch points in this trial, TABRANCH is
populated for two records in each Arm. Note also that the values of ARMCD, like the values of ARM, reflect the two separate processes that result in a subject's
assignment to an Arm.
Trial Arms Dataset for Example Trial 3
Row

STUDYID

DOMAIN

ARMCD

ARM

TAETORD

ETCD

ELEMENT

TABRANCH

1
2

EX3
EX3

TA
TA

AA
AA

A-Open A
A-Open A

1
2

SCRN
DBA

Screen
Treatment A

Randomized to Treatment A
Assigned to Open Drug A on basis of
response evaluation

EX3

A-Open A

4
5

EX3
EX3

TA
TA

AR
AR

A-Rescue
A-Rescue

1
2

SCRN
DBA

Open DRUG
A
Screen
Treatment A

6
7
8

EX3
EX3
EX3

TA
TA
TA

AR
BA
BA

A-Rescue
B-Open A
B-Open A

3
1
2

RSC
SCRN
DBB

Rescue
Screen
Treatment B

EX3

B-Open A

10
11

EX3
EX3

TA
TA

BR
BR

B-Rescue
B-Rescue

1
2

SCRN
DBB

Open DRUG
A
Screen
Treatment B

EX3

B-Rescue

RSC

Rescue

TATRANS

EPOCH

Screen
Double Blind
Open Label

Randomized to Treatment A
Assigned to Rescue on basis of
response evaluation
Randomized to Treatment B
Assigned to Open Drug A on basis of
response evaluation

Screen
Double Blind
Open Label
Screen
Double Blind
Open Label

Randomized to Treatment B
Assigned to Rescue on basis of
response evaluation

Screen
Double Blind
Open Label

See Section 7.2.4.1 for additional discussion of when a decision point in a trial design should be considered to give rise to a new Arm.
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7.2.3.4 EXAMPLE TRIAL 4, CYCLES OF CHEMOTHERAPY
The diagram below uses a new symbol, a large curved arrow representing the fact that the chemotherapy treatment
(A or B) and the rest period that follows it are to be repeated. In this trial, the chemotherapy "cycles" are to be
repeated until disease progression. Some chemotherapy trials specify a maximum number of cycles, but protocols
that allow an indefinite number of repeats are not uncommon.

Example Trial 4: Cyclical Chemotherapy
Study Schema
Repeat until disease progression

Drug A

Rest

Drug B

Rest

Screen

Randomization

The next diagram shows the prospective view of this trial. Note that, in spite of the repeating element structure, this is, at its
core, a two-arm parallel study, and thus has two arms. In SDTMIG 3.1.1, there was an implicit assumption that each element
must be in a separate epoch, and trials with cyclical chemotherapy were difficult to handle. The introduction of the concept of
study cells, and the dropping of the assumption that elements and epochs have a one to one relationship resolves these
difficulties. This trial is best treated as having just three epochs, since the main objectives of the trial involve comparisons
between the two treatments, and do not require data to be considered cycle by cycle.

Example Trial 4: Cyclical Chemotherapy
Prospective View

Treatment
Epoch

Screening
Epoch

Follow-up
Epoch

Drug A

Rest

Repeat until
disease progression

Drug A

Drug B

Rest

Repeat until
disease progression

Drug B

Screen

Randomization

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The next diagram shows the retrospective view of this trial.

Example Trial 4: Cyclical Chemotherapy
Retrospective View

Treatment
Epoch

Screening
Epoch

Follow-up
Epoch

Screen

Drug A

Rest

Repeat until
disease progression

Drug A

Screen

Drug B

Rest

Repeat until
disease progression

Drug B

Randomization

For the purpose of developing a Trial Arms dataset for this oncology trial, the diagram must be redrawn to explicitly represent
multiple treatment and rest elements. If a maximum number of cycles is not given by the protocol, then, for the purposes of
constructing an SDTM Trial Arms dataset for submission, which can only take place after the trial is complete, the number of
repeats included in the Trial Arms dataset should be the maximum number of repeats that occurred in the trial. The next
diagram assumes that the maximum number of cycles that occurred in this trial was four. Some subjects will not have received
all four cycles, because their disease progressed. The rule that directed that they receive no further cycles of chemotherapy is
represented by a set of green arrows, one at the end of each Rest epoch, that shows that a subject ―skips forward‖ if their
disease progresses. In the Trial Arms dataset, each "skip forward" instruction is a transition rule, recorded in the TATRANS
variable; when TATRANS is not populated, the rule is to transition to the next element in sequence.

Example Trial 4: Cyclical Chemotherapy
Retrospective View with Explicit Repeats
Screening
Epoch

Treatment
Epoch

Follow-up
Epoch

If disease progression

Screen

A Rest A Rest A Rest A Rest

Drug A

Drug B

If disease progression

Screen

Rest B

Rest

Randomization

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The logistics of dosing mean that few oncology trials are blinded, if this trial is blinded, then the next diagram shows
the trial from the viewpoint of blinded participants.

Example Trial 4: Cyclical Chemotherapy
Blinded View

Treatment
Epoch

Screening
Epoch

Follow-up
Epoch

If disease progression

Screen

Rx Rest Rx Rest Rx Rest Rx Rest

Blind

Randomization

The Trial Design Matrix for Example Trial 4 corresponds to the diagram showing the retrospective view with
explicit repeats of the treatment and rest elements. As noted above, the Trial Design Matrix does not include
information on when randomization occurs; similarly, information corresponding to the ―skip forward‖ rules is not
represented in the Trial Design Matrix.
Trial Design Matrix for Example Trial 4
A
B

Screen
Screen
Screen

Trt A
Trt B

Rest
Rest

Trt A
Trt B

Rest
Rest

Treatment
Trt A
Trt B

Rest
Rest

Trt A
Trt B

Rest
Rest

Follow-up
Follow-up
Follow-up

The Trial Arms dataset for Example Trial 4 requires the use of the TATRANS variable in the Trial Arms dataset to
represent the "repeat until disease progression" feature. In order to represent this rule in the diagrams that explicitly
represent repeated elements, a green "skip forward" arrow was included at the end of each element where disease
progression is assessed. In the Trial Arms dataset, TATRANS is populated for each element with a green arrow in
the diagram. In other words, if there is a possibility that a subject will, at the end of this Element, "skip forward" to a
later part of the Arm, then TATRANS is populated with the rule describing the conditions under which a subject will
go to a later element. If the subject always goes to the next Element in the Arm (as was the case for the first three
example trials presented here) then TATRANS is null.
The Trial Arms datasets presented below corresponds to the Trial Design Matrix above.

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Trial Arms Dataset for Example Trial 4
Row

STUDYID

DOMAIN

ARMCD

ARM

TAETORD

ETCD

ELEMENT

TABRANCH

1
2
3

EX4
EX4
EX4

TA
TA
TA

A
A
A

1
2
3

SCRN
A
REST

Screen
Trt A
Rest

Randomized to A

4
5

EX4
EX4

TA
TA

A
A

4
5

A
REST

Trt A
Rest

6
7

EX4
EX4

TA
TA

A
A

6
7

A
REST

Trt A
Rest

8
9
10
11
12
13

EX4
EX4
EX4
EX4
EX4
EX4

TA
TA
TA
TA
TA
TA

A
A
A
B
B
B

8
9
10
1
2
3

A
REST
FU
SCRN
B
REST

Trt A
Rest
Follow-up
Screen
Trt B
Rest

14
15

EX4
EX4

TA
TA

B
B

4
5

B
REST

Trt B
Rest

16
17

EX4
EX4

TA
TA

B
B

6
7

B
REST

Trt B
Rest

18
19

EX4
EX4

TA
TA

B
B

8
9

B
REST

Trt B
Rest

Treatment
Treatment

EX4

Follow-up

TATRANS

EPOCH

If disease progression, go to Follow-up
Epoch
If disease progression, go to Follow-up
Epoch
If disease progression, go to Follow-up
Epoch

Randomized to B
If disease progression, go to Follow-up
Epoch
If disease progression, go to Follow-up
Epoch
If disease progression, go to Follow-up
Epoch

Screen
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Follow-up
Screen
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment

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7.2.3.5 EXAMPLE TRIAL 5, CYCLES WITH DIFFERENT TREATMENT DURATIONS
Example Trial 5 is much like the last oncology trial in that the two treatments being compared are given in cycles,
and the total length of the cycle is the same for both treatments. However, in this trial Treatment A is given over
longer duration than Treatment B. Because of this difference in treatment patterns, this trial cannot be blinded.

Example Trial 5: Different Chemo Durations
Study Schema
Repeat until disease progression

Drug A

Rest

Screen

Total length Drug A cycle: 3 weeks

Rest

Follow
Total length Drug B cycle: 3 weeks

Randomization

In SDTMIG 3.1.1, the assumption of a one to one relationship between elements and epochs made this example
difficult to handle. However, without that assumption, this trial is essentially the same as Trial 4. The next diagram
shows the retrospective view of this trial.

Example Trial 5: Cyclical Chemotherapy
Retrospective View

Screening
Epoch

Treatment
Epoch

Screen

Drug A

Screen

Rest
Rest

Follow-up
Epoch

Repeat until
disease progression

Drug A

Repeat until
disease progression

Drug B

Randomization

The Trial Design Matrix for this trial is almost the same as for Example Trial 4; the only difference is that the
maximum number of cycles for this trial was assumed to be three.
Trial Design Matrix for Example Trial 5
A
B

Screen
Screen
Screen

Treatment
Trt A
Trt B

Rest A
Rest B

Trt A
Trt B

Rest A
Rest B

Trt A
Trt B

Rest A
Rest B

Follow-up
Follow-up
Follow-up

The Trial Arms dataset for this trial shown below corresponds to the Trial Design Matrix above.
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Trial Arms Dataset for Example Trial 5, with one Epoch per Cycle
Row

STUDYID

DOMAIN

ARMCD

ARM

TAETORD

ETCD

ELEMENT

TABRANCH

1
2
3

EX5
EX5
EX5

TA
TA
TA

A
A
A

1
2
3

SCRN
A
RESTA

Screen
Trt A
Rest A

Randomized to A

4
5

EX5
EX5

TA
TA

A
A

4
5

A
RESTA

Trt A
Rest A

6
7
8
9
10
11

EX5
EX5
EX5
EX5
EX5
EX5

TA
TA
TA
TA
TA
TA

A
A
A
B
B
B

6
7
8
1
2
3

A
RESTA
FU
SCRN
B
RESTB

Trt A
Rest A
Follow-up
Screen
Trt B
Rest B

12
13

EX5
EX5

TA
TA

B
B

4
5

B
RESTB

Trt B
Rest B

14
15
16

EX5
EX5
EX5

TA
TA
TA

B
B
B

6
7
8

B
RESTB
FU

Trt B
Rest B
Follow-up

TATRANS

If disease progression, go to
Follow-up Epoch
If disease progression, go to
Follow-up Epoch

Randomized to B
If disease progression, go to
Follow-up Epoch
If disease progression, go to
Follow-up Epoch

EPOCH

Screen
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Follow-up
Screen
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Follow-up

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7.2.3.6 EXAMPLE TRIAL 6, CHEMOTHERAPY TRIAL WITH CYCLES OF DIFFERENT LENGTHS
Example Trial 6 is an oncology trial comparing two types of chemotherapy that are given using cycles of different
lengths with different internal patterns. Treatment A is given in 3-week cycles with a longer duration of treatment
and a short rest, while Treatment B is given in 4-week cycles with a short duration of treatment and a long rest.

The design of this trial is very similar to that for Example Trials 4 and 5. The main difference is that there are two
different rest elements, the short one used with Drug A and the long one used with Drug B. The next diagram shows the
retrospective view of this trial.

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The Trial Design Matrix for this trial assumes that there were a maximum of four cycles of Drug A and a maximum
of three cycles of Drug B.
Trial Design Matrix for Example Trial 6
Screen Treatment
Screen
Trt A
Rest A
A
Screen
Trt
Rest B
B
B

Trt A

Rest A
Trt A
Trt
Rest B
B

Rest A
Trt
B

Trt A
Rest B

Rest A

Follow-up
Follow-up
Follow-up

In the following Trial Arms dataset, because the Treatment Epoch for Arm A has more Elements than the
Treatment Epoch for Arm B, TAETORD is 10 for the Follow-up Element in Arm A, but 8 for the Follow-up
Element in Arm B. It would also be possible to assign a TAETORD value of 10 to the Follow-up Element in Arm
B. The primary purpose of TAETORD is to order Elements within an Arm; leaving gaps in the series of
TAETORD values does not interfere with this purpose.

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Trial Arms Dataset for Example Trial 6
Row
1
2
3

STUDYID
EX6
EX6
EX6

DOMAIN
TA
TA
TA

ARMCD
A
A
A

ARM
A
A
A

TAETORD
1
2
3

ETCD
SCRN
A
RESTA

ELEMENT
Screen
Trt A
Rest A

4
5

EX6
EX6

TA
TA

A
A

4
5

A
RESTA

Trt A
Rest A

6
7

EX6
EX6

TA
TA

A
A

6
7

A
RESTA

Trt A
Rest A

8
9
10
11
12
13

EX6
EX6
EX6
EX6
EX6
EX6

TA
TA
TA
TA
TA
TA

A
A
A
B
B
B

8
9
10
1
2
3

A
RESTA
FU
SCRN
B
RESTB

Trt A
Rest A
Follow-up
Screen
Trt B
Rest B

14
15

EX6
EX6

TA
TA

B
B

4
5

B
RESTB

Trt B
Rest B

16
17
18

EX6
EX6
EX6

TA
TA
TA

B
B
B

6
7
8

B
RESTB
FU

Trt B
Rest B
Follow-up

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TABRANCH
Randomized to A

TATRANS

If disease progression, go to
Follow-up Epoch
If disease progression, go to
Follow-up Epoch
If disease progression, go to
Follow-up Epoch

Randomized to B
If disease progression, go to
Follow-up Epoch
If disease progression, go to
Follow-up Epoch

EPOCH
Screen
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Follow-up
Screen
Treatment
Treatment
Treatment
Treatment
Treatment
Treatment
Follow-up

CDISC SDTM Implementation Guide (Version 3.1.2)

7.2.3.7 EXAMPLE TRIAL 7, TRIAL WITH DISPARATE ARMS
In open trials, there is no requirement to maintain a blind, and the Arms of a trial may be quite different from each
other. In such a case, changes in treatment in one Arm may differ in number and timing from changes in treatment in
another Arm, so that there is nothing like a one-to-one match between the Elements in the different Arms. In such a
case, Epochs are likely to be defined as broad intervals of time, spanning several Elements, and be chosen to
correspond to periods of time that will be compared in analyses of the trial.
Example Trial 7, RTOG 93-09, involves treatment of lung cancer with chemotherapy and radiotherapy, with or
without surgery. The protocol (RTOG-93-09), which is available online at the Radiation Oncology Therapy Group
(RTOG) website http://www.rtog.org/members/numericactive.html , does not include a study schema diagram, but
does include a text-based representation of diverging ―options‖ to which a subject may be assigned. All subjects go
through the branch point at randomization, when subjects are assigned to either Chemotherapy + Radiotherapy (CR)
or Chemotherapy + Radiotherapy + Surgery (CRS). All subjects receive induction chemotherapy and radiation, with
a slight difference between those randomized to the two arms during the second cycle of chemotherapy. Those
randomized to the non-surgery arm are evaluated for disease somewhat earlier, to avoid delays in administering the
radiation boost to those whose disease has not progressed. After induction chemotherapy and radiation, subjects are
evaluated for disease progression, and those whose disease has progressed stop treatment, but enter follow-up. Not
all subjects randomized to receive surgery who do not have disease progression will necessarily receive surgery. If
they are poor candidates for surgery or do not wish to receive surgery, they will not receive surgery, but will receive
further chemotherapy. The diagram below is based on the text ―schema‖ in the protocol, with the five ―options‖ it
names. The diagram in this form might suggest that the trial has five arms.
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Example Trial 7: RTOG 93-09
Study schema with 5 ―options‖
Evaluation for
Disease Progression
Chemo
+ Rad

Screen

Chemo
+ Rad*

Disease evaluation
* earlier
** later
Chemo Chemo
+ Rad + Rad**

Randomization

Chemo
+ Boost

Chemo

Follow 2

Follow 5

Chemo

3-5 w
4-6 w
Surgery
Rest
Rest

Options

Chemo

Evaluation for
Disease Progression and
Surgical Eligibility

Follow 3

Follow 4

Follow 1

However, the objectives of the trial make it clear that this trial is designed to compare two treatment strategies,
chemotherapy and radiation with and without surgery, so this study is better modeled as a two-Arm trial, but with
major "skip forward" arrows for some subjects, as illustrated in the following diagram. This diagram also shows
more detail within the blocks labeled ―Induction Chemo + RT‖ and ―Additional Chemo‖ than the diagram above.
Both the ―induction‖ and ―additional‖ chemotherapy are given in two cycles. Also, the second induction cycle is
different for the two arms, since radiation therapy for those assigned to the non-surgery arm includes a ―boost‖
which those assigned to surgery arm do not receive.

CDISC SDTM Implementation Guide (Version 3.1.2)

The next diagram shows the prospective view of this trial. The protocol conceives of treatment as being divided into
two parts, Induction and Continuation, so these have been treated as two different epochs. This is also an important
point in the trial operationally, the point when subjects are ―registered‖ a second time, and when subjects are
identified who will ―skip forward‖ because of disease progression or ineligibility for surgery.

Example Trial 7: RTOG 93-09
Prospective View
Screen
Epoch

Induction
Treatment
Epoch

Continuation
Treatment Epoch

Chemotherapy
and Radiation
Arm

Follow
Epoch

If disease progression
Chemo Chemo
+ Rad + Rad*

Chemo
Chemo
+ Boost

CRS

If disease progression
Screen

If not eligible for surgery
Chemo Chemo
+ Rad + Rad**

3-5 w
4-6 w
Surgery
Rest
Rest

Chemo Chemo

Chemotherapy,
Radiation and
Surgery Arm

Randomization

The next diagram shows the retrospective view of this trial. The fact that the elements in the study cell for the CR arm in the
Continuation Treatment Epoch do not fill the space in the diagram is an artifact of the diagram conventions. Those subjects
who do receive surgery will in fact spend a longer time completing treatment and moving into follow-up. Although it is
tempting to think of the horizontal axis of these diagrams as a timeline, this can sometimes be misleading. The diagrams are
not necessarily ―to scale‖ in the sense that the length of the block representing an element represents its duration, and elements
that line up on the same vertical line in the diagram may not occur at the same relative time within the study.

Example Trial 7: RTOG 93-09
Retrospective View
Screen
Epoch

Induction
Treatment
Epoch

Continuation
Treatment Epoch

Follow
Epoch

If disease progression

Screen

Chemo Chemo
+ Rad + Rad*

Chemo
Chemo
+ Boost

CRS

If disease progression
If not eligible for surgery
Screen

Chemo Chemo
+ Rad + Rad**

3-5 w
4-6 w
Surgery
Rest
Rest

Chemo Chemo

Randomization

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The Trial Design Matrix for Example Trial 7, RTOG 93-09, a Two-Arm Trial is shown in the following table.

Screen
Screen

CRS

Screen

Induction
Initial
Chemo + RT
Initial
Chemo + RT

Chemo + RT
(non-Surgery)
Chemo + RT
(Surgery)

Continuation
Chemo
3-5 w
Rest

Surgery

Chemo
4-6 w
Rest

Chemo

Follow-up
Off Treatment
Follow-up
Off Treatment Followup

The Trial Arms dataset for the trial is shown below for Example Trial 7, as a two-Arm trial
Row

STUDYID

DOMAIN

ARMCD

ARM

TAETORD

ETCD

ELEMENT

TABRANCH

1
2
3

EX7
EX7
EX7

TA
TA
TA

1
1
1

CR
CR
CR

1
2
3

SCRN
ICR
CRNS

Screen
Initial Chemo + RT
Chemo+RT (non-Surgery)

Randomized to CR

4
5
6
7
8
9

EX7
EX7
EX7
EX7
EX7
EX7

TA
TA
TA
TA
TA
TA

1
1
1
2
2
2

CR
CR
CR
CRS
CRS
CRS

4
5
6
1
2
3

C
C
FU
SCRN
ICR
CRS

Chemo
Chemo
Off Treatment Follow-up
Screen
Initial Chemo + RT
Chemo+RT (Surgery)

10
11
12
13
14
15

EX7
EX7
EX7
EX7
EX7
EX7

TA
TA
TA
TA
TA
TA

2
2
2
2
2
2

CRS
CRS
CRS
CRS
CRS
CRS

4
5
6
7
8
9

R3
SURG
R4
C
C
FU

3-5 week rest
Surgery
4-6 week rest
Chemo
Chemo
Off Treatment Follow-up

TATRANS

EPOCH

If progression, skip to
Follow-up.

Randomized to CRS
If progression, skip to
Follow-up. If no
progression, but
subject is ineligible for
or does not consent to
surgery, skip to Addl
Chemo.

Screen
Induction
Induction
Continuation
Continuation
Follow-up
Screen
Induction
Induction

Continuation
Continuation
Continuation
Continuation
Continuation
Follow-up

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7.2.4 ISSUES IN TRIAL ARMS DATASETS
7.2.4.1 DISTINGUISHING BETWEEN BRANCHES AND TRANSITIONS
Both the Branch and Transition columns contain rules, but the two columns represent two different types of rules.
Branch rules represent forks in the trial flowchart, giving rise to separate Arms. The rule underlying a branch in
the trial design appears in multiple records, once for each "fork" of the branch. Within any one record, there is no
choice (no "if" clause) in the value of the Branch condition. For example, the value of TABRANCH for a record
in Arm A is "Randomized to Arm A" because a subject in Arm A must have been randomized to Arm A.
Transition rules are used for choices within an Arm. The value for TATRANS does contain a choice (an "if"
clause). In Example Trial 4, subjects who receive 1, 2, 3, or 4 cycles of Treatment A are all considered to belong
to Arm A.
In modeling a trial, decisions may have to be made about whether a decision point in the flow chart represents
the separation of paths that represent different Arms, or paths that represent variations within the same Arm, as
illustrated in the discussion of Example Trial 7. This decision will depend on the comparisons of interest in the
trial.
Some trials refer to groups of subjects who follow a particular path through the trial as "cohorts", particularly if
the groups are formed successively over time. The term "cohort" is used with different meanings in different
protocols and does not always correspond to an Arm.
7.2.4.2 SUBJECTS NOT ASSIGNED TO AN ARM
Some trial subjects may drop out of the study before they reach all of the branch points in the trial design. In the
Demographics domain, values of ARM and ARMCD must be supplied for such subjects, but the special values
used for these subjects should not be included in the Trial Arms dataset; only complete Arm paths should be
described in the Trial Arms dataset. Demographics Assumption 4 (Section 5.1.1.1) describes special ARM and
ARMCD values used for subjects who do not reach the first branch point in a trial. When a trial design includes
two or more branches, special values of ARM and ARMCD may be needed for subjects who pass through the
first branch point, but drop out before the final branch point. See Example 7 in Section 5.1.1.2 for an example of
how to construct values of ARM and ARMCD for such trials.
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7.2.4.3 DEFINING EPOCHS
The series of examples in Section 7.2.3 provides a variety of scenarios and guidance about how to assign Epoch
in those scenarios. In general, assigning Epochs for blinded trials is easier than for unblinded trials. The blinded
view of the trial will generally make the possible choices clear. For unblinded trials, the comparisons that will be
made between Arms can guide the definition of Epochs. For trials that include many variant paths within an Arm,
comparisons of Arms will mean that subjects on a variety of paths will be included in the comparison, and this is
likely to lead to definition of broader Epochs.
1369H

7.2.4.4 RULE VARIABLES
The Branch and Transition columns shown in the example tables are variables with a Role of ―Rule.‖ The values
of a Rule variable describe conditions under which something is planned to happen. At the moment, values of
Rule variables are text. At some point in the future, it is expected that these will become executable code. Other
Rule variables are present in the Trial Elements and Trial Visits datasets.

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7.3 TRIAL ELEMENTS
The Trial Elements (TE) dataset contains the definitions of the elements that appear in the Trial Arms (TA) dataset.
An Element may appear multiple times in the Trial Arms table because it appears either 1) in multiple Arms, 2)
multiple times within an Arm, or 3) both. However, an Element will appear only once in the Trial Elements table.
Each row in the TE dataset may be thought of as representing a "unique Element" in the sense of "unique" used
when a case report form template page for a collecting certain type of data is often referred to as "unique page." For
instance, a case report form might be described as containing 87 pages, but only 23 unique pages. By analogy, the
trial design matrix for Example Trial 1 in Section 7.2.3.1 has 9 Study Cells, each of which contains one Element,
but the same trial design matrix contains only 5 unique Elements, so the trial Elements dataset for that trial has only
5 records.
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An Element is a building block for creating Study Cells and an Arm is composed of Study Cells. Or, from another
point of view, an Arm is composed of Elements, i.e., the trial design assigns subjects to Arms, which are comprised
of a sequence of steps called Elements.
Trial Elements represent an interval of time that serves a purpose in the trial and are associated with certain activities
affecting the subject. ―Week 2 to Week 4‖ is not a valid Element. A valid Element has a name that describes the
purpose of the Element and includes a description of the activity or event that marks the subject's transition into the
Element as well as the conditions for leaving the Element.

7.3.1 TRIAL ELEMENTS DATASET — TE
te.xpt, Trial Elements — Trial Design, Version 3.1.2 One record per planned Element
Controlled
Terms,
Variable Label
Type
Role
CDISC Notes
Core
Codelist or
Format
STUDYID Study Identifier
Char
Identifier Unique identifier for a study.
Req
DOMAIN Domain Abbreviation Char TE
Identifier Two-character abbreviation for the domain.
Req
ETCD
Element Code
Char *
Topic
ETCD (the companion to ELEMENT) is limited to 8
Req
characters and does not have special character restrictions.
These values should be short for ease of use in
programming, but it is not expected that ETCD will need
to serve as a variable name.
ELEMENT Description of Element Char *
Synonym The name of the Element.
Req
Qualifier
TESTRL Rule for Start of
Char
Rule
Expresses rule for beginning Element.
Req
Element
TEENRL Rule for End of Element Char
Rule
Expresses rule for ending Element. Either TEENRL or
Perm
TEDUR must be present for each Element.
TEDUR
Planned Duration of
Char ISO 8601 Timing
Planned Duration of Element in ISO 8601 format. Used Perm
Element
when the rule for ending the Element is applied after a
fixed duration.
Variable
Name

201H

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

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7.3.2 ASSUMPTIONS FOR TE DATASET
1.
2.

3.
4.

6.
7.

8.
9.
10.

11.

12.
13.

14.

15.

There are no gaps between Elements. The instant one Element ends, the next Element begins. A subject spends
no time ―between‖ Elements.
ELEMENT, the Description of the Element, usually indicates the treatment being administered during an Element, or,
if no treatment is being administered, the other activities that are the purpose of this period of time, such as Screening,
Follow-up, Washout. In some cases, this may be quite passive, such as Rest, or Wait (for disease episode).
TESTRL, the Rule for Start of Element, identifies the event that marks the transition into this Element. For
Elements that involve treatment, this is the start of treatment.
For Elements that do not involve treatment, TESTRL can be more difficult to define. For washout and follow-up
Elements, which always follow treatment Elements, the start of the Element may be defined relative to the end
of a preceding treatment. For example, a washout period might be defined as starting 24 or 48 hours after the
last dose of drug for the preceding treatment Element or Epoch. This definition is not totally independent of the
Trial Arms dataset, since it relies on knowing where in the trial design the Element is used, and that it always
follows a treatment Element. Defining a clear starting point for the start of a non-treatment Element that always
follows another non-treatment Element can be particularly difficult. The transition may be defined by a
decision-making activity such as enrollment or randomization. For example, every Arm of a trial which
involves treating disease episodes might start with a screening Element followed by an Element which consists
of waiting until a disease episode occurs. The activity that marks the beginning of the wait Element might be
randomization.
TESTRL for a treatment Element may be thought of as ―active‖ while the start rule for a non-treatment
Element, particularly a follow-up or washout Element, may be ―passive.‖ The start of a treatment Element will
not occur until a dose is given, no matter how long that dose is delayed. Once the last dose is given, the start of
a subsequent non-treatment Element is inevitable, as long as another dose is not given.
Note that the date/time of the event described in TESTRL will be used to populate the date/times in the Subject
Elements dataset, so the date/time of the event should be one that will be captured in the CRF.
Specifying TESTRL for an Element that serves the first Element of an Arm in the Trial Arms dataset involves
defining the start of the trial. In the examples in this document, obtaining informed consent has been used as
"Trial Entry."
TESTRL should be expressed without referring to Arm. If the Element appears in more than one Arm in the
Trial Arms dataset, then the Element description (ELEMENT) must not refer to any Arms.
TESTRL should be expressed without referring to Epoch. If the Element appears in more than one Epoch in the
Trial Arms dataset, then the Element description (ELEMENT) must not refer to any Epochs.
For a blinded trial, it is useful to describe TESTRL in terms that separate the properties of the event that are
visible to blinded participants from the properties that are visible only to those who are unblinded. For treatment
Elements in blinded trials, wording such as the following is suitable, "First dose of study drug for a treatment
Epoch, where study drug is X."
Element end rules are rather different from Element start rules. The actual end of one Element is the beginning
of the next Element. Thus the Element end rule does not give the conditions under which an Element does end,
but the conditions under which it should end or is planned to end.
At least one of TEENRL and TEDUR must be populated. Both may be populated.
TEENRL describes the circumstances under which a subject should leave this Element. Element end rules may
depend on a variety of conditions. For instance, a typical criterion for ending a rest Element between oncology
chemotherapy-treatment Elements would be, ―15 days after start of Element and after WBC values have
recovered.‖ The Trial Arms dataset, not the Trial Elements dataset, describes where the subject moves next, so
TEENRL must be expressed without referring to Arm.
TEDUR serves the same purpose as TEENRL for the special (but very common) case of an Element with a
fixed duration. TEDUR is expressed in ISO 8601. For example, a TEDUR value of P6W is equivalent to a
TEENRL of "6 weeks after the start of the Element."
Note that Elements that have different start and end rules are different Elements and must have different values
of ELEMENT and ETCD. For instance, Elements that involve the same treatment but have different durations
are different Elements. The same applies to non-treatment Elements. For instance, a washout with a fixed
duration of 14 days is different from a washout that is to end after 7 days if drug cannot be detected in a blood
sample, or after 14 days otherwise.

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7.3.3 TRIAL ELEMENTS EXAMPLES
Below are Trial Elements datasets for Example Trials 1 and 2 described in Section 7.2.3.1 and Section 7.2.3.2. Both
these trials are assumed to have fixed-duration Elements. The wording in TESTRL is intended to separate the
description of the event that starts the Element into the part that would be visible to a blinded participant in the trial
(e.g., "First dose of a treatment Epoch") from the part that is revealed when the study is unblinded (e.g., "where dose
is 5 mg"). Care must be taken in choosing these descriptions to be sure that they are "Arm and Epoch neutral." For
instance, in a crossover trial such as Example Trial 3 described in Section 7.2.3.3, where an Element may appear in
one of multiple Epochs, the wording must be appropriate for all the possible Epochs. The wording for Example Trial
2 uses the wording "a treatment Epoch." The SDS Team is considering adding a separate variable to the Trial
Elements dataset that would hold information on the treatment that is associated with an Element. This would make
it clearer which Elements are "treatment Elements‖, and therefore, which Epochs contain treatment Elements, and
thus are "treatment Epochs".
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Trial Elements Dataset for Example Trial 1
Row
1
2
3

STUDYID
EX1
EX1
EX1

DOMAIN
TE
TE
TE

ETCD
SCRN
RI
P

ELEMENT
Screen
Run-In
Placebo

EX1

Drug A

EX1

Drug B

TESTRL
Informed consent
Eligibility confirmed
First dose of study drug,
where drug is placebo
First dose of study drug,
where drug is Drug A
First dose of study drug,
where drug is Drug B

TEENRL
1 week after start of Element
2 weeks after start of Element
2 weeks after start of Element

TEDUR
P7D
P14D
P14D

2 weeks after start of Element

P14D

2 weeks after start of Element

P14D

TESTRL

TEENRL

TEDUR

Informed consent
First dose of a treatment
Epoch, where dose is
placebo
First dose of a treatment
Epoch, where dose is 5 mg
drug
First dose of a treatment
Epoch, where dose is 10
mg drug
48 hrs after last dose of
preceding treatment Epoch
48 hrs after last dose of
third treatment Epoch

2 weeks after start of Element
2 weeks after start of Element

P14D
P14D

2 weeks after start of Element

P14D

2 weeks after start of Element

P14D

1 week after start of Element

P7D

3 weeks after start of Element

P21D

Trial Elements Dataset for Example Trial 2
Row

STUDYID

DOMAIN

ETCD

1
2

EX2
EX2

TE
TE

SCRN
P

ELEME
NT
Screen
Placebo

EX2

5 mg

EX2

10 mg

EX2

REST

Rest

EX2

Follow-up

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The Trial Elements dataset for Example Trial 4 illustrates Element end rules for Elements that are not of fixed
duration. The Screen Element in this study can be up to 2 weeks long, but may end earlier, so is not of fixed
duration. The Rest Element has a variable length, depending on how quickly WBC recovers. Note that the start rules
for the A and B Elements have been written to be suitable for a blinded study.
Trial Elements Dataset for Example Trial 4
Row

STUDYID

DOMAIN

ETCD

ELEMENT

TESTRL

TEENRL

TEDUR

EX4

SCRN

Screen

Informed Consent

EX4

Trt A

P5D

EX4

Trt B

5 days after start of Element

P5D

EX4

REST

Rest

EX4

Follow-up

First dose of treatment Element,
where drug is Treatment A
First dose of treatment Element,
where drug is Treatment B
Last dose of previous treatment
cycle + 24 hrs
Decision not to treat further

Screening assessments are
complete, up to 2 weeks after
start of Element
5 days after start of Element

At least 16 days after start of
Element and WBC recovered
4 weeks

P28D

7.3.4 TRIAL ELEMENTS ISSUES
7.3.4.1 GRANULARITY OF TRIAL ELEMENTS
Deciding how finely to divide trial time when identifying trial Elements is a matter of judgment, as illustrated by the
following examples:
1. Example Trial 2 (described in Section 7.2.3.2, and with Elements described in Section 7.3.3) was represented
using three treatment Epochs separated by two washout Epochs and followed by a follow-up Epoch. It might
have been modeled using three treatment Epochs that included both the 2-week treatment period and the 1-week
rest period. Since the first week after the third treatment period would be included in the third treatment Epoch,
the Follow-up Epoch would then have a duration of 2 weeks.
2. In Example Trials 4, 5, and 6 in Section 7.2.3.4, Section 7.2.3.5 and Section 7.2.3.6 separate Treatment and Rest
Elements were identified. However, the combination of treatment and rest could be represented as a single
Element.
3. A trial might include a dose titration, with subjects receiving increasing doses on a weekly basis until certain
conditions are met. The trial design could be modeled in any of the following ways:
 using several one-week Elements at specific doses, followed by an Element of variable length at the
chosen dose,
 as a titration Element of variable length followed by a constant dosing Element of variable length
 one Element with dosing determined by titration
The choice of Elements used to represent this dose titration will depend on the objectives of the trial and how
the data will be analyzed and reported. If it is important to examine side effects or lab values at each individual
dose, the first model is appropriate. If it is important only to identify the time to completion of titration, the
second model might be appropriate. If the titration process is routine and is of little interest, the third model
might be adequate for the purposes of the trial.
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7.3.4.2 DISTINGUISHING ELEMENTS, STUDY CELLS, AND EPOCHS
It is easy to confuse Elements, which are reusable trial building blocks, with Study Cells, which contain the
Elements for a particular Epoch and Arm, and with Epochs, which are time periods for the trial as a whole. In part,
this is because many trials have Epochs for which the same Element appears in all Arms. In other words, in the trial
design matrix for many trials, there are columns (Epochs) in which all the Study Cells have the same contents.
Furthermore, it is natural to use the same name (e.g., Screen or Follow-up) for both such an Epoch and the single
Element that appears within it.
Confusion can also arise from the fact that, in the blinded treatment portions of blinded trials, blinded participants do
not know which Element a subject is in, but do know what Epoch the subject is in.

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In describing a trial, one way to avoid confusion between Elements and Epochs is to include "Element" or "Epoch" in
the values of ELEMENT or EPOCH when these values (such as Screening or Follow-up) would otherwise be the same.
It becomes tedious to do this in every case, but can be useful to resolve confusion when it arises or is likely to arise.
The difference between Epoch and Element is perhaps clearest in crossover trials. In Example Trial 2, as for most crossover
trials, the analysis of PK results would include both treatment and period effects in the model. ―Treatment effect‖ derives from
Element (Placebo, 5 mg, or 10 mg), while ―Period effect‖ derives from the Epoch (1st, 2nd, or 3rd Treatment Epoch).
7.3.4.3 TRANSITIONS BETWEEN ELEMENTS
The transition between one Element and the next can be thought of as a three-step process:
Step
Step Question
How step question is answered by information in the Trial Design datasets
Number
1
Should the subject leave
Criteria for ending the current Element are in TEENRL in the TE dataset.
the current Element?
2
Which Element should the  If there is a branch point at this point in the trial, evaluate criteria described in
subject enter next?
TABRANCH (e.g., randomization results) in the TA dataset
 otherwise, if TATRANS in the TA dataset is populated in this Arm at this
point, follow those instructions
 otherwise, move to the next Element in this Arm as specified by TAETORD in
the TA dataset.
3
What does the subject do The action or event that marks the start of the next Element is specified in
to enter the next Element? TESTRL in the TE dataset
Note that the subject is not "in limbo" during this process. The subject remains in the current Element until Step 3, at
which point the subject transitions to the new Element. There are no gaps between Elements.
From this table, it is clear that executing a transition depends on information that is split between the Trial Elements
and the Trial Arms datasets.
It can be useful, in the process of working out the Trial Design datasets, to create a dataset that supplements the Trial
Arms dataset with the TESTRL, TEENRL, and TEDUR variables, so that full information on the transitions is easily
accessible. However, such a working dataset is not an SDTM dataset, and should not be submitted.
The following table shows a fragment of such a table for Example Trial 4. Note that for all records that contain a particular
Element, all the TE variable values are exactly the same. Also, note that when both TABRANCH and TATRANS are
blank, the implicit decision in Step 2 is that the subject moves to the next Element in sequence for the Arm.
ARM EPOCH

TAETORD

ELEMENT TESTRL

Screen

Treatment 2

Trt A

Treatment 3

Rest

Treatment 4

Trt A

TEENRL

Screening assessments
are complete, up to 2
weeks after start of
Element
First dose of treatment in 5 days after start of
Element, where drug is Element
Treatment A
Last dose of previous
16 days after start of
treatment cycle + 24 hrs Element and WBC
recovers
First dose of treatment in 5 days after start of
Element, where drug is Element
Treatment A

TEDUR TABRANCH TATRANS

Informed Consent

Randomized
to A

P5D

If disease
progression, go to
Follow-up Epoch
P5D

Note that both the second and fourth rows of this dataset involve the same Element, Trt A, and so TESTRL is the
same for both. The activity that marks a subject's entry into the fourth Element in Arm A is "First dose of treatment
Element, where drug is Treatment A." This is not the subject's very first dose of Treatment A, but it is their first dose
in this Element.
© 2008 Clinical Data Interchange Standards Consortium, Inc. All rights reserved
Final

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7.4 TRIAL VISITS
The Trial Visits (TV) dataset describes the planned Visits in a trial. Visits are defined as "clinical encounters" and are
described using the timing variables VISIT, VISITNUM, and VISITDY.
Protocols define Visits in order to describe assessments and procedures that are to be performed at the Visits.

7.4.1 TRIAL VISITS DATASET — TV
tv.xpt, Trial Visits — Trial Design, Version 3.1.2. One record per planned Visit per Arm
Controlled
Terms,
Variable Label
Type
Role
CDISC Notes
Codelist or
Format
STUDYID Study Identifier
Char
Identifier Unique identifier for a study.
DOMAIN Domain Abbreviation Char TV
Identifier Two-character abbreviation for the domain
VISITNUM Visit Number
Num
Topic
1. Clinical encounter number
2. Numeric version of VISIT, used for sorting.
VISIT
Visit Name
Char
Synonym 1. Protocol-defined description of clinical encounter.
Qualifier 2. May be used in addition to VISITNUM and/or VISITDY
as a text description of the clinical encounter.
VISITDY
Planned Study Day of Num
Timing
1. Planned study day of VISIT.
Visit
2. Due to its sequential nature, used for sorting.
ARMCD
Planned Arm Code
Char *
Record
1.ARMCD is limited to 20 characters and does not
Qualifier have special character restrictions. The maximum
Variable
Name

203H

Core
Req
Req
Req
Perm

Perm
Exp

length of ARMCD is longer than for other ―short‖
variables to accommodate the kind of values that are
likely to be needed for crossover trials. For example,
if ARMCD values for a seven-period crossover were
constructed using two-character abbreviations for
each treatment and separating hyphens, the length of
ARMCD values would be 20.

ARM

Description of Planned Char
Arm

TVSTRL

Visit Start Rule

Char

TVENRL

Visit End Rule

Char

2. If the timing of Visits for a trial does not depend on
which ARM a subject is in, then ARMCD should be null.
Synonym 1. Name given to an Arm or Treatment Group.
Qualifier 2. If the timing of Visits for a trial does not depend on
which Arm a subject is in, then Arm should be left blank.
Rule
Rule describing when the Visit starts, in relation to the
sequence of Elements.
Rule
Rule describing when the Visit ends, in relation to the
sequence of Elements.

Perm

Req
Perm

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

7.4.2 ASSUMPTIONS FOR TV DATASET
1.

Although the general structure of the Trial Visits dataset is "One Record per Planned Visit per Arm", for many
clinical trials, particularly blinded clinical trials, the schedule of Visits is the same for all Arms, and the structure of
the Trial Visits dataset will be "One Record per Planned Visit". If the schedule of Visits is the same for all Arms,
ARMCD should be left blank for all records in the TV dataset. For trials with trial Visits that are different for
different Arms, such as Example Trial 7 in Section 7.2.3.7, ARMCD and ARM should be populated for all records.
If some Visits are the same for all Arms, and some Visits differ by Arm, then ARMCD and ARM should be
populated for all records, to assure clarity, even though this will mean creating near-duplicate records for Visits that
are the same for all Arms.
A Visit may start in one Element and end in another. This means that a Visit may start in one Epoch and end in
another. For example, if one of the activities planned for a Visit is the administration of the first dose of study drug,
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the Visit might start in the screen Epoch, in the screen Element, and end in a treatment Epoch, in a treatment
Element.
TVSTRL describes the scheduling of the Visit and should reflect the wording in the protocol. In many trials, all
Visits are scheduled relative to the study's Day 1, RFSTDTC. In such trials, it is useful to include VISITDY,
which is, in effect, a special case representation of TVSTRL.
Note that there is a subtle difference between the following two examples. In the first case, if Visit 3 were delayed for
some reason, Visit 4 would be unaffected. In the second case, a delay to Visit 3 would result in Visit 4 being delayed
as well.




Case 1: Visit 3 starts 2 weeks after RFSTDTC. Visit 4 starts 4 weeks after RFSTDTC.
Case 2: Visit 3 starts 2 weeks after RFSTDTC. Visit 4 starts 2 weeks after Visit 3.
Many protocols do not give any information about Visit ends because Visits are assumed to end on the same day
they start. In such a case, TVENRL may be left blank to indicate that the Visit ends on the same day it starts. Care
should be taken to assure that this is appropriate, since common practice may be to record data collected over more
than one day as occurring within a single Visit. Screening Visits may be particularly prone to collection of data over
multiple days. See Section 7.4.3 for examples showing how TVENRL could be populated.
The values of VISITNUM in the TV dataset are the valid values of VISITNUM for planned Visits. Any values of
VISITNUM that appear in subject-level datasets that are not in the TV dataset are assumed to correspond to
unplanned Visits. This applies, in particular, to the subject-level Subject Visits (SV) dataset; see Section 5.3.2 on
the SV dataset for additional information about handling unplanned Visits. If a subject-level dataset includes both
VISITNUM and VISIT, then records that include values of VISITNUM that appear in the TV dataset should also
include the corresponding values of VISIT from the TV dataset.
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7.4.3 TRIAL VISITS EXAMPLES
The diagram below shows Visits by means of numbered "flags" with Visit Numbers. Each "flag" has two supports,
one at the beginning of the Visit, the other at the end of the Visit. Note that Visits 2 and 3 span Epoch transitions. In
other words, the transition event that marks the beginning of the Run-in Epoch (confirmation of eligibility) occurs
during Visit 2, and the transition event that marks the beginning of the Treatment Epoch (the first dose of study
drug) occurs during Visit 3.

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Two Trial Visits datasets are shown for this trial. The first shows a somewhat idealized situation, where the protocol has given
specific timings for the Visits. The second shows a more usual situation, where the timings have been described only loosely.
Trial Visits Dataset for Example Trial 1 with explicitly scheduled starts and ends of Visits
Row
1
2
3
4
5

STUDYID
EX1
EX1

DOMAIN
TV
TV

VISITNUM
1
2

EX1
EX1
EX1

TV
TV
TV

3
4
5

TVSTRL
Start of Screen Epoch
30 minutes before end of Screen Epoch

TVENRL
1 hour after start of Visit
30 minutes after start of Run-in
Epoch
30 minutes before end of Run-in Epoch 1 hour after start of Treatment Epoch
1 week after start of Treatment Epoch
1 hour after start of Visit
2 weeks after start of Treatment Epoch 1 hour after start of Visit

Trial Visits Dataset for Example Trial 1 with loosely described starts and ends of Visits
Row
1
2

STUDYID
EX1
EX1

DOMAIN
TV
TV

VISITNUM
1
2

EX1

4
5

EX1
EX1

TV
TV

4
5

TVSTRL
Start of Screen Epoch
On the same day as, but before, the end
of the Screen Epoch
On the same day as, but before, the end
of the Run-in Epoch
1 week after start of Treatment Epoch
2 weeks after start of Treatment Epoch

TVENRL
On the same day as, but after, the start
of the Run-in Epoch
On the same day as, but after, the start
of the Treatment Epoch
At Trial Exit

Although the start and end rules in this example reference the starts and ends of Epochs, the start and end rules of
some Visits for trials with Epochs that span multiple Elements will need to reference Elements rather than Epochs.
When an Arm includes repetitions of the same Element, it may be necessary to use TAETORD as well as an Element
name to specify when a Visit is to occur.

7.4.4 TRIAL VISITS ISSUES
7.4.4.1 IDENTIFYING TRIAL VISITS
In general, a trial's Visits are defined in its protocol. The term ―Visit‖ reflects the fact that data in outpatient studies
is usually collected during a physical Visit by the subject to a clinic. Sometimes a Trial Visit defined by the protocol
may not correspond to a physical Visit. It may span multiple physical Visits, as when screening data may be
collected over several clinic Visits but recorded under one Visit name (VISIT) and number (VISITNUM). A Trial
Visit may also represent only a portion of an extended physical Visit, as when a trial of in-patients collects data
under multiple Trial Visits for a single hospital admission.
Diary data and other data collected outside a clinic may not fit the usual concept of a Trial Visit, but the planned
times of collection of such data may be described as ―Visits‖ in the Trial Visits dataset if desired.
7.4.4.2 TRIAL VISIT RULES
Visit start rules are different from Element start rules because they usually describe when a Visit should occur, while
Element start rules describe the moment at which an Element is considered to start. There are usually gaps between
Visits, periods of time that do not belong to any Visit, so it is usually not necessary to identify the moment when one
Visit stops and another starts. However, some trials of hospitalized subjects may divide time into Visits in a manner
more like that used for Elements, and a transition event may need to be defined in such cases.
Visit start rules are usually expressed relative to the start or end of an Element or Epoch, e.g., ‖1-2 hours before end
of First Wash-out‖ or ―8 weeks after end of 2nd Treatment Epoch.‖ Note that the Visit may or may not occur during
the Element used as the reference for Visit start rule. For example, a trial with Elements based on treatment of
disease episodes might plan a Visit 6 months after the start of the first treatment period, regardless of how many
disease episodes have occurred.
Visit end rules are similar to Element end rules, describing when a Visit should end. They may be expressed relative
to the start or end of an Element or Epoch, or relative to the start of the Visit.
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The timings of Visits relative to Elements may be expressed in terms that cannot be easily quantified. For instance, a
protocol might instruct that at a baseline Visit the subject be randomized, given study drug, and instructed to take the
first dose of study Drug X at bedtime that night. This baseline Visit is thus started and ended before the start of the
treatment Epoch, but we don't know how long before the start of the treatment Epoch the Visit will occur. The trial
start rule might contain the value, "On the day of, but before, the start of the Treatment Epoch."
7.4.4.3 VISIT SCHEDULES EXPRESSED WITH RANGES
Ranges may be used to describe the planned timing of Visits (e.g., 12-16 days after the start of 2nd Element), but
this is different from the ―windows‖ that may be used in selecting data points to be included in an analysis
associated with that Visit. For example, although Visit 2 was planned for 12-16 days after the start of treatment, data
collected 10-18 days after the start of treatment might be included in a ―Visit 1― analysis. The two ranges serve
different purposes.
7.4.4.4 CONTINGENT VISITS
Section 5.3.2, which describes the Subject Visits dataset, describes how records for unplanned Visits are
incorporated. It is sometimes difficult to decide exactly what constitutes an "unplanned Visit" versus a "contingent
Visit, " a Visit that is contingent on a "trigger" event, such as a certain adverse event, a finding above a certain
threshold value, or a decision to discontinue a subject‘s participation in the trial. Contingent Visits may be included
in the Trial Visits table, with start rules that describe the circumstances under which they will take place. Since
values of VISITNUM must be assigned to all records in the Trial Visits dataset, a contingent Visit included in the
Trial Visits dataset must have a VISITNUM, but the VISITNUM value may not be a "chronological" value, due to
the uncertain timing of the Visit.
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7.5 TRIAL INCLUSION/EXCLUSION CRITERIA
The Trial Inclusion Exclusion (TI) dataset is not subject oriented. It contains all the inclusion and exclusion criteria
for the trial, and thus provides information that may not be present in the subject-level data on inclusion and
exclusion criteria. The IE domain (described in Section 6.3.2) contains records only for inclusion and exclusion
criteria that subjects did not meet.
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7.5.1 TRIAL INCLUSION/EXCLUSION CRITERIA DATASET — TI
ti.xpt, Trial Inclusion/Exclusion Criteria — Trial Design, Version 3.1.2.One record per I/E criterion
Variable
Name

Variable Label

Controlled
Terms,
Codelist or
Format

Type

STUDYID

Study Identifier

DOMAIN
IETESTCD

Domain Abbreviation Char
Incl/Excl Criterion Char
Short Name

IETEST

Inclusion/Exclusion Char
Criterion

IECAT

Inclusion/Exclusion
Category
Inclusion/Exclusion
Subcategory

Char

(IECAT)

Char

Inclusion/Exclusion
Criterion Rule
Protocol Criteria
Versions

Char

IESCAT

TIRL
TIVERS

Char

TI
*
204H

205H

Role

CDISC Notes

Core

Identifier Unique identifier for a study.

Req

Identifier Two-character abbreviation for the domain.
Topic
Short name IETEST. It can be used as a column name
when converting a dataset from a vertical to a horizontal
format. The value in IETESTCD cannot be longer than
8 characters, nor can it start with a number (e.g.,
―1TEST‖). IETESTCD cannot contain characters other
than letters, numbers, or underscores. The prefix ―IE‖ is
used to ensure consistency with the IE domain.
Synonym Full text of the inclusion or exclusion criterion. The
Qualifier prefix ―IE‖ is used to ensure consistency with the IE
domain.
Grouping Used for categorization of the inclusion or exclusion
Qualifier criteria.
Grouping A further categorization of the exception criterion. Can
Qualifier be used to distinguish criteria for a sub-study or for to
categorize as a major or minor exceptions. Examples:
MAJOR, MINOR.
Rule
Rule that expresses the criterion in computer-executable
form (see assumption 4 below).
Record
The number of this version of the Inclusion/Exclusion
Qualifier criteria. May be omitted if there is only one version.

Req
Req

Req

Req
Perm

Perm
Perm

* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)

7.5.2 ASSUMPTIONS FOR TI DATASET
1.

If inclusion/exclusion criteria were amended during the trial, then each complete set of criteria must be included
in the TI domain. TIVERS is used to distinguish between the versions.

Protocol version numbers should be used to identify criteria versions, though there may be more versions of the
protocol than versions of the inclusion/exclusion criteria. For example, a protocol might have versions 1, 2, 3, and 4,
but if the inclusion/exclusion criteria in version 1 were unchanged through versions 2 and 3, and only changed in
version 4, then there would be two sets of inclusion/exclusion criteria in TI, one for version 1 and one for version 4.

Individual criteria do not have versions. If a criterion changes, it should be treated as a new criterion, with a
new value for IETESTCD. If criteria have been numbered and values of IETESTCD are generally of the form
INCL00n or EXCL00n, and new versions of a criterion have not been given new numbers, separate values of
IETESTCD might be created by appending letters, e.g. INCL003A, INCL003B.

IETEST contains the text of the inclusion/exclusion criterion. However, since entry criteria are rules, the
variable TIRL has been included in anticipation of the development of computer executable rules.

If a criterion text is <200 characters, it goes in IETEST; if the text is >200 characters, put meaningful text in
IETEST and describe the full text in the study metadata. See Section 4.1.5.3.1 for further information.
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7.5.3 EXAMPLES FOR TRIAL INCLUSION/EXCLUSION DATASET MODEL
This example shows records for a trial that had two versions of inclusion/exclusion criteria.
Rows 1-3 show the two inclusion criteria and one exclusion criterion for version 1 of the protocol.
Rows 4-6 show the inclusion/exclusion criteria for version 2.2 of the protocol, which changed the minimum age for
entry from 21 to 18.
Row STUDYID DOMAIN
XYZ
TI
1
XYZ
TI
2
XYZ
TI
3
XYZ
TI
4
XYZ
TI
5
XYZ
TI
6

IETESTCD
INCL01
INCL02
EXCL01
INCL01
INCL02A
EXCL01

IETEST
Has disease under study
Age 21 or greater
Pregnant or lactating
Has disease under study
Age 18 or greater
Pregnant or lactating

IECAT
INCLUSION
INCLUSION
EXCLUSION
INCLUSION
INCLUSION
EXCLUSION

TIVERS
1
1
1
2.2
2.2
2.2

7.6 TRIAL SUMMARY INFORMATION
The Trial Summary (TS) dataset allows the sponsor to submit a summary of the trial in a structured format. Each
record in the Trial Summary dataset contains the value of a parameter, a characteristic of the trial. For example, Trial
Summary is used to record basic information about the study such as trial phase, protocol title, and trial objectives.
The Trial Summary dataset contains information about the planned trial characteristics; it does not contain subject
level data or data that can be derived from subject data. Thus, for example, it includes the number of subjects
planned for the trial but not the number of subjects enrolled in the trial.

7.6.1 TRIAL SUMMARY DATASET — TS
ts.xpt, Trial Summary — Trial Design, Version 3.1.2.One record per trial summary parameter value
Variable
Name

STUDYID
DOMAIN
TSSEQ

Controlled
Terms,
Variable Label Type
Codelist or
Format

Study
Char
Identifier
Domain
Char TS
Abbreviation
Sequence
Num
Number
206H

Role

CDISC Notes

Core

Identifier Unique identifier for a study.

Req

Identifier Two-character abbreviation for the domain.

Req

Identifier Sequence number given to ensure uniqueness within a
dataset. Allows inclusion of multiple records for the same
TSPARMCD, and can be used to join related records.
TSGRPID
Group ID
Char
Identifier Used to tie together a group of related records
TSPARMCD Trial
Char TSPARMCD Topic
TSPARMCD (the companion to TSPARM) is limited to 8
Summary
characters and does not have special character restrictions.
Parameter
These values should be short for ease of use in programming,
Short Name
but it is not expected that TSPARMCD will need to serve as
variable names. Examples: AGEMIN, AGEMAX
TSPARM
Trial
Char TSPARM
Synonym Term for the Trial Summary Parameter. The value in
Summary
Qualifier TSPARM cannot be longer than 40 characters. Examples
Parameter
Planned Minimum Age of Subjects, Planned Maximum Age
of Subjects
TSVAL
Parameter
Char *
Result
Value of TSPARM. Example: ―ASTHMA‖ when TSPARM
Value
Qualifier value is ―Trial Indication‖. TSVAL cannot be null – a value is
required for the record to be valid. Text over 200 characters
can be added to additional columns TSVAL1-TSVALn.
* Indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code value)
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Req

Perm
Req

Req

CDISC SDTM Implementation Guide (Version 3.1.2)

7.6.2 ASSUMPTIONS FOR TRIAL SUMMARY DATASET MODEL
1.
2.
3.
4.

5.
6.

7.
8.

The intent of this dataset is to provide a summary of trial information. This is not subject level data.
A list of values for TSPARM and TSPARMCD is included in Appendix C3. The appendix also includes
assumptions related to particular parameters.
TSVAL may have controlled terminology depending on the value of TSPARMCD. See Appendix C3 for more
information.
There is not yet guidance on which Trial Summary parameters are required, but the following minimum
recommended set is based on the WHO International Clinical Trial Registry Platform (ICTRP) Registration
Data Set: TITLE, INDIC, TCNTRL, RANDOM, TRT, COMPTRT (when applicable), AGESPAN, AGEMIN,
AGEMAX, AGEU, SEXPOP, PLANSUB, OBJPRIM, OBJSEC. Parameters to support TRT (e.g., DOSE,
ROUTE, etc.) should be considered. However, for some complex study designs, these simple parameters may
not provide a useful summary of the trial design.
Sponsors may include parameters not in Appendix C3. The meaning of such parameters should be explained
in the metadata for the TS dataset.
For some trials, there will be multiple records in the Trial Summary dataset for a single parameter. For
example, a trial that addresses both Safety and Efficacy could have two records with TSPARMCD = TTYPE,
one with the TSVAL = "SAFETY" and the other with TSVAL = "EFFICACY."
TSSEQ has a different value for each record for the same parameter. Note that this is different from datasets
that contain subject data, where the --SEQ variable has a different value for each record for the same subject.
The method for treating text > 200 characters in Trial Summary is similar to that used for the Comments
special-purpose domain (Section 5.2). If TSVAL is > 200 characters, then it should be split into multiple
variables, TSVAL-TSVALn.
Since TS does not contain subject-level data, there is no restriction analogous to the requirement in subjectlevel datasets that the blocks bound by TSGRPID are within a subject. TSGRPID can be used to tie together
any block of records in the dataset. GRPID is most likely to be used when the TS dataset includes multiple
records for the same parameter. For example, if a trial compared a dose of 50 mg twice a day with a dose of
100 mg once a day, a record with TSPARMCD = DOSE and TSVAL=50 and a record with TSPARMCD =
DOSFREQ and TSVAL = BID could be assigned one GRPID, while a record with TSPARMCD = DOSE and
TSVAL=100 and a record with TSPARMCD = DOSFREQ and TSVAL = Q24H could be assigned a different
GRPID.
The order of parameters in the examples of TD datasets in Section 7.6.3 should not be taken as a requirement.
There are no requirements or expectations about the order of parameters within the TS dataset.
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7.6.3 EXAMPLES FOR TRIAL SUMMARY DATASET MODEL
Example 1:
Rows 1-5, 10-13,
15 and 16:
Use controlled terminology for TSVAL (see Appendix C3).
Rows 1-6, 8-11,
13 and 14:
Contain parameters from the recommended minimal set. The recommended (but not required)
parameters OBJSEC, TCNTRL, and PLANSUB are missing.
Rows 1-2:
This trial includes subjects from both the ADULT (18-65) and ELDERLY (>65) age groups, so
there are two records for the AGESPAN parameter.
Row 7:
The parameter DESIGN, which is not included in Appendix C3, was added by the Sponsor.
Rows 15-16:
This trial addresses both safety and efficacy, so there are 2 records for the TYPE parameter.
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1392H

Row
1
2
3

STUDYID
XYZ
XYZ
XYZ

DOMAIN
TS
TS
TS

TSSEQ
1
2
1

TSPARMCD
AGESPAN
AGESPAN
AGEMAX

TSPARM
Age Span
Age Span
Planned Maximum Age of
Subjects
Planned Minimum Age of
Subjects
Age Unit
Comparative Treatment
Name
Description of Trial Design
Trial Indication

XYZ

AGEMIN

5
6

XYZ
XYZ

TS
TS

1
1

AGEU
COMPTRT

7
8
9

XYZ
XYZ
XYZ

TS
TS
TS

1
1
1

DESIGN
INDIC
OBJPRIM

10
11
12
13

XYZ
XYZ
XYZ
XYZ

TS
TS
TS
TS

1
1
1
1

RANDOM
SEXPOP
TBLIND
TITLE

Trial is Randomized
Sex of Participants
Trial Blinding Schema
Trial Title

XYZ

TRT

15
16

XYZ
XYZ

TS
TS

1
2

TTYPE
TTYPE

Reported Name of Test
Product
Trial Type
Trial Type

Trial Primary Objective

TSVAL
ADULT (18-65)
ELDERLY (> 65)
70
18
YEARS
PLACEBO
PARALLEL
Asthma
Reduce the incidence of
exacerbations of asthma
Y
BOTH
DOUBLE BLIND
A 24 Week Study of Daily
Oral Investigational Drug vs.
Placebo in Subjects with
Asthma
Investigational New Drug
EFFICACY
SAFETY

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Example 2
Rows 1-3: AGEMIN, AGEMAX and AGEU are included, but AGESPAN is missing.
Row 5:
The parameter DESIGN, which is not included in Appendix C3, was added by the Sponsor.
Row 7:
Note that TSVAL for the LENGTH parameter is expressed in ISO 8601 duration format. Note that
TSVAL for LENGTH is P14W, while the TSVAL for TITLE includes "10-week." The trial involved 10
weeks of treatment, but the planned total duration of a subject's involvement in the study was 14
weeks.
Row 9:
The parameter PLANEVAL is not in Appendix C3, but was added by the sponsor.
Row 15:
The title is longer than 200 characters, so it has been separated into two pieces and stored in TSVAL
and TSVAL1.
Row 15:
The title includes the information that dosing in the study was flexible. The sponsor felt this flexible
dosing could not be adequately represented using the usual dosing parameters, so none were submitted.
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Row

STUDYID

DOMAIN

TSSEQ

TSPARMCD

TSPARM

TSVAL

ABC

AGEMIN

ABC

AGEMAX

3
4

ABC
ABC

TS
TS

1
1

AGEU
COMPTRT

ABC

DESIGN

6
7
8

ABC
ABC
ABC

TS
TS
TS

1
1
1

INDIC
LENGTH
PLANSUB

ABC

PLANEVAL

ABC

SEXPOP

ABC

RANDOM

ABC

TBLIND

13
14

ABC
ABC

TS
TS

1
1

TCNTRL
TINDTP

ABC

TITLE

Planned Minimum
Age of Subjects
Planned
Maximum Age of
Subjects
Age Unit
Comparative
Treatment Name
Description of
Trial Design
Trial Indication
Trial Length
Planned Number
of Subjects
Planned Number
of Evaluable
Subjects
Sex of
Participants
Trial is
Randomized
Trial Blinding
Schema
Type of Control
Trial Indication
Type
Trial Title

ABC

TPHASE

ABC

TRT

18
19

ABC
ABC

TS
TS

1
2

TTYPE
TTYPE

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Trial Phase
Classification
Reported Name of
Test Product
Trial Type
Trial Type

TSVAL1

YEARS

PLACEBO
Parallel
Generalized Disease
P14W
500
470

BOTH
Y
DOUBLE BLIND
PLACEBO
TREATMENT
A 10-Week,
Randomized, DoubleBlind, PlaceboControlled, ParallelGroup, FlexibleDosage Study to
Evaluate the Efficacy
and Safety of New
Drug (up to
16 mg/day) in the
Treatment of
Phase III Trial

Adults With
Generalized
Disease

New Drug
EFFICACY
SAFETY

CDISC SDTM Implementation Guide (Version 3.1.2)

Example 3
Rows 2-6, 11, 13-17,
19, 22, 25, and 26:

Contain all the recommended minimum set of parameters except COMPTRT. Since the
TCNTL record indicates that this trial is placebo-controlled, the sponsor decided that
COMPTRT would be redundant, and did not submit it.
Rows 7 and 8:
Since this is a trial comparing two doses, there are two DOSE records.
Rows 9, 10 and 17:
These rows provide unit, frequency, and route data to support the DOSE records.
Row 12:
Note that for the LENGTH parameter, TSVAL is expressed in ISO 8601 duration format.
Standard terminology for TSVAL is in upper case.
Rows 13, 14, and 24: This sponsor has chosen to submit all TSVAL values, including objectives and title, in upper
case.
Row
1

STUDYID
DEF

DOMAIN
TS

TSSEQ
1

TSPARMCD
ADDON

2
3
4

DEF
DEF
DEF

TS
TS
TS

1
2
1

AGESPAN
AGESPAN
AGEMAX

DEF

AGEMIN

6
7
8
9
10
11
12
13

DEF
DEF
DEF
DEF
DEF
DEF
DEF
DEF

TS
TS
TS
TS
TS
TS
TS
TS

1
1
2
1
1
1
1
1

AGEU
DOSE
DOSE
DOSFRQ
DOSEU
INDIC
LENGTH
OBJPRIM

TSPARM
Added on to Existing
Treatments
Age Group
Age Group
Planned Maximum Age of
Subjects
Planned Minimum Age of
Subjects
Age Unit
Dose per Administration
Dose per Administration
Frequency
Dose Unit
Trial Indication
Trial Length
Trial Primary Objective

DEF

OBJSEC

Trial Secondary Objective

DEF

PLANSUB

16
17
18
19
20
21
22

DEF
DEF
DEF
DEF
DEF
DEF
DEF

TS
TS
TS
TS
TS
TS
TS

1
1
1
1
1
1
1

RANDOM
ROUTE
SEXPOP
SPONSOR
TBLIND
TCNTRL
TDIGRP

Planned Number of
Subjects
Trial is Randomized
Route of Administration
Sex of Participants
Sponsoring Organization
Trial Blinding Schema
Type of Control
Diagnosis Group

23
24

DEF
DEF

TS
TS

1
1

TINDTP
TITLE

Trial Indication Type
Trial Title

25
26

DEF
DEF

TS
TS

1
1

TPHASE
TRT

27
28

DEF
DEF

TS
TS

1
2

TTYPE
TTYPE

Trial Phase Classification
Reported Name of Test
Product
Trial Type
Trial Type

TSVAL
N
ADULT (18-65)
ELDERLY (> 65)
75
22
YEARS
100
200
BID
mg
TEST INDICATION
P30M
TO INVESTIGATE THE
SAFETY AND EFFICACY OF
TWO DOSES
COMPARE SAFETY
PROFILES OF TWO DOSES
210
Y
ORAL
BOTH
SPONSOR NAME
DOUBLE BLIND
PLACEBO
SUBJECTS DIAGNOSED
WITH DISEASE
TREATMENT
A RANDOMIZED, DOUBLEBLIND, PLACEBOCONTROLLED, MULTICENTER, PARALLEL GROUP
DOSE RANGING STUDY.
PHASE III TRIAL
STUDY DRUG
SAFETY
EFFICACY

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7.7 HOW TO MODEL THE DESIGN OF A CLINICAL TRIAL
The following steps allow the modeler to move from more-familiar concepts, such as Arms, to less-familiar
concepts, such as Elements and Epochs. The actual process of modeling a trial may depart from these numbered
steps. Some steps will overlap, there may be several iterations, and not all steps are relevant for all studies.
1.
2.

4.
5.
6.

8.
9.

10.

Start from the flow chart or schema diagram usually included in the trial protocol. This diagram will show
how many Arms the trial has, and the branch points, or decision points, where the Arms diverge.
Write down the decision rule for each branching point in the diagram. Does the assignment of a subject to
an Arm depend on a randomization? On whether the subject responded to treatment? On some other
criterion?
If the trial has multiple branching points, check whether all the branches that have been identified really
lead to different Arms. The Arms will relate to the major comparisons the trial is designed to address. For
some trials, there may be a group of somewhat different paths through the trial that are all considered to
belong to a single Arm.
For each Arm, identify the major time periods of treatment and non-treatment a subject assigned to that
Arm will go through. These are the Elements, or building blocks, of which the Arm is composed.
Define the starting point of each Element. Define the rule for how long the Element should last. Determine
whether the Element is of fixed duration.
Re-examine the sequences of Elements that make up the various Arms and consider alternative Element
definitions. Would it be better to ―split‖ some Elements into smaller pieces or ―lump‖ some Elements into
larger pieces? Such decisions will depend on the aims of the trial and plans for analysis.
Compare the various Arms. In most clinical trials, especially blinded trials, the pattern of Elements will be
similar for all Arms, and it will make sense to define Trial Epochs. Assign names to these Epochs. During
the conduct of a blinded trial, it will not be known which Arm a subject has been assigned to, or which
treatment Elements they are experiencing, but the Epochs they are passing through will be known.
Identify the Visits planned for the trial. Define the planned start timings for each Visit, expressed relative to
the ordered sequences of Elements that make up the Arms. Define the rules for when each Visit should end.
Identify the inclusion and exclusion criteria to be able to populate the TI dataset. If inclusion and exclusion
criteria were amended so that subjects entered under different versions, populate TIVERS to represent the
different versions.
Populate the TS dataset with summary information.

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8 Representing Relationships
and Data
The defined variables of the SDTM general observation classes could restrict the ability of sponsors to represent all
the data they wish to submit. Collected data that may not entirely fit includes relationships between records within a
domain, records in separate domains, and sponsor-defined ―variables.‖ As a result, the SDTM has methods to
represent five distinct types of relationships, all of which are described in more detail in subsequent sections. These
include the following:


Section 8.1 describes a relationship between a group of records for a given subject within the same dataset.
1395H



Section 8.2 describes a relationship between independent records (usually in separate datasets) for a
subject, such as a concomitant medication taken to treat an adverse event.
1396H



Section 8.3 describes a relationship between two (or more) datasets where records of one (or more)
dataset(s) are related to record(s) in another dataset (or datasets).
1397H



Section 8.4 describes a method for representing the dependent relationship where data that cannot be
represented by a standard variable within a general-observation-class dataset record (or records) can be
related back to that record.
1398H



Section 8.5 describes a dependent relationship between a comment in the Comments domain (see also
Section 5.2) and a parent record (or records) in other datasets, such as a comment recorded in association
with an adverse event.
139H

140H



Section 8.6 discusses the concept of related datasets and whether to place additional data in a separate
dataset or a Supplemental Qualifier special-purpose dataset, and the concept of modeling Findings data that
refers to data in another general-observation-class dataset.
140H

All relationships make use of the standard domain identifiers, STUDYID, DOMAIN, and USUBJID. In addition, the
variables IDVAR and IDVARVAL are used for identifying the record-level merge/join keys. These keys are used to
tie information together by linking records. The specific set of identifiers necessary to properly identify each type of
relationship is described in detail in the following sections. Examples of variables that could be used in IDVAR
include the following variables:
 The Sequence Number (--SEQ) variable uniquely identifies a record for a given USUBJID within a
domain. The variable --SEQ is required in all domains except DM. For example, if subject 1234-2003 has
25 adverse event records in the adverse event (AE) domain, then 25 unique AESEQ values should be
established for this subject. Conventions for establishing and maintaining --SEQ values are sponsordefined. Values may or may not be sequential depending on data processes and sources.


The Reference Identifier (--REFID) variable can be used to capture a sponsor-defined or external identifier,
such as an identifier provided in an electronic data transfer. Some examples are lab-specimen identifiers
and ECG identifiers. --REFID is permissible in all domains, but never required. Values for --REFID are
sponsor -defined and can be any alphanumeric strings the sponsor chooses, consistent with their internal
practices.



The Grouping Identifier (--GRPID) variable, used to link related records for a subject within a dataset, is
explained below in Section 8.1.
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8.1

RELATING GROUPS OF RECORDS WITHIN A DOMAIN USING
THE --GRPID VARIABLE

The optional grouping identifier variable --GRPID is permissible in all domains that are based on the general
observation classes. It is used to identify relationships between records within a USUBJID within a single domain.
An example would be Intervention records for a combination therapy where the treatments in the combination varies
from subject to subject. In such a case, the relationship is defined by assigning the same unique character value to
the --GRPID variable. The values used for --GRPID can be any values the sponsor chooses; however, if the sponsor
uses values with some embedded meaning (rather than arbitrary numbers), those values should be consistent across
the submission to avoid confusion. It is important to note that --GRPID has no inherent meaning across subjects or
across domains.
Using --GRPID in the general-observation-class datasets can reduce the number of records in the RELREC, SUPP--,
and CO datasets when those datasets are submitted to describe relationships/associations for records or values to a
―group‖ of general-observation-class records.

8.1.1 --GRPID EXAMPLE
The following table illustrates how to use --GRPID in the Concomitant Medications (CM) domain to identify a
combination therapy. In this example, both subjects 1234 and 5678 have reported two combination therapies, each
consisting of three separate medications. Each component of a combination is given the same value for CMGRPID.
Note that for USUBJID 1234, the medications for CMGRPID = ―COMBO THPY 1‖ (Rows 1-3) are different from the
medications for CMGRPID = ―COMBO THPY 2‖ (Rows 4-6). Likewise, for USUBJID 5678, the medications for
CMGRPID = ―COMBO THPY 1‖ (Rows 7-9) are different from the medications for CMGRPID = ―COMBO THPY 2‖
(Rows 10-12). Additionally, the medications for Subject 1234 CMGRPID = ―COMBO THPY 1‖ and CMGRPID =
―COMBO THPY 2‖ (Rows 1-6) are different from the medications for Subject 5678 CMGRPID = ―COMBO THPY 1‖
and CMGRPID = ―COMBO THPY 2‖ (Rows 7-12). This example illustrates how CMGRPID groups information only
within a subject within a domain.
Row STUDYID DOMAIN USUBJID CMSEQ CMGRPID CMTRT
1

1234

5678

1234

5678

1234

5678

1234

5678

1234

5678

1234

5678

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November 12, 2008

COMBO
THPY 1
COMBO
THPY 1
COMBO
THPY 1
COMBO
THPY 2
COMBO
THPY 2
COMBO
THPY 2
COMBO
THPY 1
COMBO
THPY 1
COMBO
THPY 1
COMBO
THPY 2
COMBO
THPY 2
COMBO
THPY 2

CMDECOD CMDOSE CMDOSU

Verbatim Generic Med
Med A
A
Verbatim Generic Med
Med B
B
Verbatim Generic Med
Med C
C
Verbatim Generic Med
Med D
D
Verbatim Generic Med
Med E
E
Verbatim Generic Med
Med F
F
Verbatim Generic Med
Med G
G
Verbatim Generic Med
Med H
H
Verbatim Generic Med I
Med I
Verbatim Generic Med
Med J
J
Verbatim Generic Med
Med K
K
Verbatim Generic Med
Med L
L

CMSTDTC CMENDTC

100

2004-01-17

2004-01-19

2004-01-17

2004-01-19

200

2004-01-17

2004-01-19

150

2004-01-21

2004-01-22

100

2004-01-21

2004-01-22

2004-01-21

2004-01-22

37.5

2004-03-17

2004-03-25

2004-03-17

2004-03-25

2004-03-17

2004-03-25

100

2004-03-21

2004-03-22

2004-03-21

2004-03-22

2004-03-21

2004-03-22

CDISC SDTM Implementation Guide (Version 3.1.2)

8.2 RELATING PEER RECORDS
The Related Records (RELREC) special-purpose dataset is used to describe relationships between records for a
subject (as described in this section), and relationships between datasets (as described in Section 8.3). In both cases,
relationships represented in RELREC are collected relationships, either by explicit references or check boxes on the
CRF, or by design of the CRF, such as vital signs captured during an exercise stress test.
1403H

A relationship is defined by adding a record to RELREC for each record to be related and by assigning a unique
character identifier value for the relationship. Each record in the RELREC special-purpose dataset contains keys that
identify a record (or group of records) and the relationship identifier, which is stored in the RELID variable. The value
of RELID is chosen by the sponsor, but must be identical for all related records within USUBJID. It is recommended
that the sponsor use a standard system or naming convention for RELID (e.g., all letters, all numbers, capitalized).
Records expressing a relationship are specified using the key variables STUDYID, RDOMAIN (the two-letter domain
code of the record in the relationship), and USUBJID, along with IDVAR and IDVARVAL. Single records can be
related by using a unique-record-identifier variable such as --SEQ in IDVAR. Groups of records can be related by
using grouping variables such as --GRPID in IDVAR. IDVARVAL would contain the value of the variable described
in IDVAR. Using --GRPID can be a more efficient method of representing relationships in RELREC, such as when
relating an adverse event (or events) to a group of concomitant medications taken to treat the adverse event(s).
The RELREC dataset should be used to represent either:
 Explicit relationships, such as concomitant medications taken as a result of an adverse event.
 Information of a nature that necessitates using multiple datasets, as described in Section 8.3.
140H

8.2.1 RELREC DATASET
relrec.xpt, Related Records, Version 3.1.2. One record per related record, group of records or dataset
Controlled
Type Terms, Codelist
CDISC Notes
or Format
STUDYID Study Identifier Char
Unique identifier for a study
RDOMAIN Related Domain Char DOMAIN
Two-character abbreviation for the domain of the parent
Abbreviation
record(s)
USUBJID Unique Subject Char
Identifier used to uniquely identify a subject across all
Identifier
studies for all applications or submissions involving the
product.
IDVAR
Identifying
Char *
Name of the identifying variable in the generalVariable
observation-class dataset that identifies the related
record(s). Examples include --SEQ and --GRPID.
IDVARVAL Identifying
Char
Value of identifying variable described in IDVAR. If
Variable Value
--SEQ is the variable being used to describe this record,
then the value of --SEQ would be entered here.
RELTYPE Relationship
Char ONE, MANY
Identifies the hierarchical level of the records in the
Type
relationship. Values should be either ONE or MANY.
Used only when identifying a relationship between
datasets (as described in Section 8.3).
RELID
Relationship
Char
Unique value within USUBJID that identifies the
Identifier
relationship. All records for the same USUBJID that have
the same RELID are considered ―related/associated.‖
RELID can be any value the sponsor chooses, and is only
meaningful within the RELREC dataset to identify the
related/associated Domain records.
Variable

Variable Label

1405H

Core
Req
Req

References

SDTMIG
Appendix C2
1406H

Exp

Req

Exp

1407H

Req

*indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code
value)

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8.2.2 RELREC DATASET EXAMPLES
Example 1:
This example shows how to use the RELREC dataset to relate records stored in separate domains for USUBJID
123456 who had two lab tests performed (Rows 5 and 6) and took two concomitant medications (Rows 2 and 3) as
the result of an adverse event (Rows 1 and 4). This example represents a situation in which the adverse event is
related to both the concomitant medications and the lab tests, but there is no relationship between the lab values and
the concomitant medications
Row
1
2
3
4
5
6

STUDYID
EFC1234
EFC1234
EFC1234
EFC1234
EFC1234
EFC1234

RDOMAIN
AE
CM
CM
AE
LB
LB

USUBJID
123456
123456
123456
123456
123456
123456

IDVAR
AESEQ
CMSEQ
CMSEQ
AESEQ
LBSEQ
LBSEQ

IDVARVAL
5
11
12
5
47
48

RELTYPE

RELID
1
1
1
2
2
2

Example 2:
Example 2 is the same scenario as Example 1; however, the relationship between concomitant medications (Rows 2
and 3) and lab values (Rows 4 and 5) and their relationship with the adverse event (Row 1) was collected.
Row
1
2
3
4
5

STUDYID

RDOMAIN

USUBJID

IDVAR

IDVARVAL

EFC1234
EFC1234
EFC1234
EFC1234
EFC1234

AE
CM
CM
LB
LB

123456
123456
123456
123456
123456

AESEQ
CMSEQ
CMSEQ
LBSEQ
LBSEQ

5
11
12
47
48

RELTYPE

RELID
1
1
1
1
1

Example 3:
Example 3 is the same scenario as Example 2. However, the two concomitant medications have been grouped by the
sponsor in the CM dataset by assigning a CMGRPID of ―COMBO 1‖, allowing the elimination of a record in the
RELREC dataset.
Row
1
2
3
4

STUDYID
EFC1234
EFC1234
EFC1234
EFC1234

RDOMAIN
AE
CM
LB
LB

USUBJID
123456
123456
123456
123456

IDVAR
AESEQ
CMGRPID
LBSEQ
LBSEQ

IDVARVAL
5
COMBO1
47
48

RELTYPE

RELID
1
1
1
1

Additional examples may be found in the domain examples such as the examples for Disposition/Adverse Event
found in Section 6.2.2.2, Example 4, and all of the Pharmacokinetics examples in Section 6.3.10.5.
1408H

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CDISC SDTM Implementation Guide (Version 3.1.2)

8.3 RELATING DATASETS
The Related Records (RELREC) special-purpose dataset can also be used to identify relationships between datasets
(e.g., a one-to-many or parent-child relationship). The relationship is defined by including a single record for each
related dataset that identifies the key(s) of the dataset that can be used to relate the respective records.
Relationships between datasets should only be recorded in the RELREC dataset when the sponsor has found it
necessary to split information between datasets that are related, and that may need to be examined together for
analysis or proper interpretation. Note that it is not necessary to use the RELREC dataset to identify associations
from data in the SUPP-- datasets or the CO dataset to their parent general-observation-class dataset records or
special-purpose domain records, as both these datasets include the key variable identifiers of the parent record(s)
that are necessary to make the association.

8.3.1 RELREC DATASET RELATIONSHIP EXAMPLE
This example shows how to use the RELREC dataset to represent related information that is submitted as two
datasets that have a one-to-many relationship. In the example below all the records in one domain are being related
to all of the records in the other, so both USUBJID and IDVARVAL are null.
Row
1
2

STUDYID
EFC1234
EFC1234

RDOMAIN
MB
MS

USUBJID

IDVAR
MBGRPID
MSGRPID

IDVARVAL

RELTYPE
ONE
MANY

RELID
A
A

In the sponsor's operational database, these datasets may have existed as either separate datasets that were merged
for analysis, or one dataset that may have included observations from more than one general observation class (e.g.,
Events and Findings). The value in IDVAR must be the name of the key used to merge/join the two datasets. In the
above example, the --GRPID variable is used as the key to identify the related observations. The values for the
--GRPID variable in the two datasets are sponsor defined. Although other variables may also serve as a single merge
key when the corresponding values for IDVAR are equal, --GRPID, --SPID, or --REFID are typically used for this
purpose.
The variable RELTYPE identifies the type of relationship between the datasets. The allowable values are ONE and
MANY (controlled terminology is expected). This information defines how a merge/join would be written, and what
would be the result of the merge/join. The possible combinations are the following:
1.

ONE and ONE. This combination indicates that there is NO hierarchical relationship between the datasets
and the records in the datasets. Only one record from each dataset will potentially have the same value of
the IDVAR within USUBJID.
ONE and MANY. This combination indicates that there IS a hierarchical (parent/child) relationship
between the datasets. One record within USUBJID in the dataset identified by RELTYPE=ONE will
potentially have the same value of the IDVAR with many (one or more) records in the dataset identified
by RELTYPE=MANY.
MANY and MANY. This combination is unusual and challenging to manage in a merge/join, and may
represent a relationship that was never intended to convey a usable merge/join (such as in described for
PC and PP in Section 6.3.10.5).
140H

Since IDVAR identifies the keys that can be used to merge/join records between the datasets, the root values (i.e.,
SPID in the above example) for IDVAR must be the same for both records with the same RELID. --SEQ cannot be
used because --SEQ only has meaning within a subject within a dataset, not across datasets.

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8.4 RELATING NON-STANDARD VARIABLES VALUES TO A PARENT
DOMAIN
The SDTM does not allow the addition of new variables. Therefore, the Supplemental Qualifiers special purpose
dataset model is used to capture non-standard variables and their association to parent records in generalobservation-class datasets (Events, Findings, Interventions) and Demographics. Supplemental Qualifiers may be
represented as either a single SUPPQUAL dataset per study or via separate SUPP-- datasets for each dataset
containing sponsor-defined variables (see Section 8.4.2 for more on this topic). Most references in this guide will
use the designation of SUPP-- rather than SUPPQUAL to serve as a reminder of the preferred submission format for
Supplemental Qualifiers.
14H

SUPP-- represents the metadata and data for each non-standard variable/value combination. As the name
"Supplemental Qualifiers" suggests, this dataset is intended to capture additional Qualifiers for an observation. Data
that represent separate observations should be treated as separate observations, either in this domain or another
domain. The Supplemental Qualifiers dataset is structured similarly to the RELREC dataset, in that it uses the same
set of keys to identify parent records. Each SUPP-- record also includes the name of the Qualifier variable being
added (QNAM), the label for the variable (QLABEL), the actual value for each instance or record (QVAL), the
origin (QORIG) of the value (see Section 4.1.1.8), and the Evaluator (QEVAL) to specify the role of the individual
who assigned the value (such as ADJUDICATION COMMITTEE or SPONSOR). Controlled terminology for
certain expected values for QNAM and QLABEL are included in Appendix C5.
142H

207H

SUPP-- datasets are also used to capture attributions. An attribution is typically an interpretation or subjective
classification of one or more observations by a specific evaluator, such as a population flag that classifies a subject or their
data according to their evaluability for efficacy analysis, or whether an observation is considered to be clinically
significant. Since it is possible that different attributions may be necessary in some cases, SUPP-- provides a mechanism
for incorporating as many attributions as are necessary. A SUPP-- dataset can contain both objective data (where values
are collected or derived algorithmically) and subjective data (attributions where values are assigned by a person or
committee). For objective data, the value in QEVAL will be null. For subjective data (when QORIG=‖ASSIGNED‖), the
value in QEVAL should reflect the role of the person or institution assigning the value (e.g., SPONSOR or
ADJUDICATION COMMITTEE).
The combined set of values for the first six columns (STUDYID…QNAM) should be unique for every record. That
is, there should not be multiple records in a SUPP-- dataset for the same QNAM value, as it relates to
IDVAR/IDVARVAL for a USUBJID in a domain. For example, if two individuals provide a determination on
whether an Adverse Event is Treatment Emergent (e.g., the investigator and an independent adjudicator) then
separate QNAM values should be used for each set of information, perhaps AETRTEMI and AETRTEMA. This is
necessary to ensure that reviewers can join/merge/transpose the information back with the records in the original
domain without risk of losing information.
When populating a SUPPDM dataset with population flags related to the Demographics domain (subject-level
evaluability), there should be one record for each population flag for each subject. QVAL values for population flags
should be Y or N, with no null values. In the event that evaluability is based upon individual visits or CRF pages,
additional population flags attached to other domains may be included in SUPP-- datasets.
Just as use of the optional grouping identifier variable, --GRPID, can be a more efficient method of representing
relationships in RELREC, it can also be used in a SUPP-- dataset to identify individual qualifier values (SUPP-records) related to multiple general-observation-class domain records that could be grouped, such as relating an
attribution to a group of ECG measurements.

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8.4.1 SUPPLEMENTAL QUALIFIERS: SUPPQUAL OR SUPP-- DATASETS
supp--.xpt, Supplemental Qualifiers [DOMAIN NAME], Version 3.1.2. One record per IDVAR, IDVARVAL,
and QNAM value per subject
Variable

Variable
Label

STUDYID

Study
Identifier
RDOMAIN Related
Domain
Abbreviation
USUBJID
Unique
Subject
Identifier
IDVAR
Identifying
Variable
IDVARVAL Identifying
Variable Value
QNAM
Qualifier
Variable Name

Controlled
Type Terms, Codelist
CDISC Notes
or Format
Char
Study Identifier of the Parent record(s).

Req

SDTM 2.2.4

Char DOMAIN

Two-character abbreviation for the domain of the parent
record(s).

Req

Char

Unique Subject Identifier of the Parent record(s).

Req

SDTM 2.2.4,
SDTMIG
Appendix C2
SDTM 2.2.4

Char *

Identifying variable in the dataset that identifies the related Exp
record(s). Examples: --SEQ, --GRPID.
Value of identifying variable of the parent record(s).
Exp

143H

Char
Char *

QLABEL

Qualifier
Char
Variable Label

QVAL

Data Value

Char

QORIG

Origin

Char

QEVAL

Evaluator

Char *

Core

The short name of the Qualifier variable, which is used as
a column name in a domain view with data from the parent
domain. The value in QNAM cannot be longer than 8
characters, nor can it start with a number (e.g., ―1TEST‖).
QNAM cannot contain characters other than letters,
numbers, or underscores. This will often be the column
name in the sponsor‘s operational dataset.
This is the long name or label associated with QNAM. The
value in QLABEL cannot be longer than 40 characters.
This will often be the column label in the sponsor‘s
original dataset.
Result of, response to, or value associated with QNAM. A
value for this column is required; no records can be in
SUPP-- with a null value for QVAL.
Since QVAL can represent a mixture of collected (on a
CRF), derived, or assigned items, QORIG is used to
indicate the origin of this data. Examples include CRF,
ASSIGNED, or DERIVED. See Section 4.1.1.8.
Used only for results that are subjective (e.g., assigned by
a person or a group). Should be null for records that
contain objectively collected or derived data. Some
examples include ADJUDICATION COMMITTEE,
STATISTICIAN, DATABASE ADMINISTRATOR,
CLINICAL COORDINATOR, etc.

References

14H

Req

SDTMIG
4.1.2.1,
Appendix C5
145H

208H

Req

146H

Exp

*indicates variable may be subject to controlled terminology, (Parenthesis indicates CDISC/NCI codelist code
value)
A record in a SUPP-- dataset relates back to its parent record(s) via the key identified by the STUDYID, RDOMAIN,
USUBJID and IDVAR/IDVARVAL variables. An exception is SUPP-- dataset records that are related to Demographics
(DM) records, such as the Intent To Treat (ITT) and Safety (SAFETY) subject-level population flags, where both IDVAR
and IDVARVAL will be null because the key variables STUDYID, RDOMAIN, and USUBJID are sufficient to identify
the unique parent record in DM (DM has one record per USUBJID).
All records in the SUPP-- datasets must have a value for QVAL. Transposing source variables with missing/null values
may generate SUPP-- records with null values for QVAL, causing the SUPP-- datasets to be extremely large. When this
happens, the sponsor must delete the records where QVAL is null prior to submission.
See Section 4.1.5.3 for information on representing information greater than 200 characters in length.
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See Appendix C5 for controlled terminology for QNAM and QLABEL for some of the most common Supplemental
Qualifiers. Additional QNAM values may be created as needed, following the guidelines provided in the CDISC
Notes for QVAL.
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8.4.2 SUBMITTING SUPPLEMENTAL QUALIFIERS IN SEPARATE DATASETS
In SDTMIG V3.1.1, the preferred approach is to submit Supplemental Qualifiers by domain rather than placing all of the
supplemental information within one dataset. Therefore, it is recommended that sponsors who utilize the single
SUPPQUAL approach begin to transition to individual SUPP-- datasets by domain. The single SUPPQUAL dataset option
will be deprecated (phased out) in the next (post V3.1.2) release.
There is a one-to-one correspondence between a domain dataset and it's Supplemental Qualifier dataset by creating one
SUPPQUAL for each domain dataset. The set of Supplemental Qualifiers for each domain is included in a separate
dataset with the name SUPP-- where ―--― denotes the source domain which the Supplemental Qualifiers relate back to. For
example, population flags and other demographic Qualifiers would be placed in suppdm.xpt. Data may have been
additionally split into multiple datasets (see Section 4.1.1.7, Splitting Domains).
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Sponsors must, however, choose only one approach for each study. Either individual SUPP-- datasets for each domain
where needed should be submitted, or a single SUPPQUAL dataset for the entire study. In other words, separate SUPP-datasets cannot be used with some domains and SUPPQUAL for the others.

8.4.3 SUPP-- EXAMPLES
The examples below demonstrate how a set of SUPP-- datasets could be used to relate non-standard information to a
parent domain.
Example 1
In the two rows of suppdm.xpt, population flags are defined as supplemental information to a subject‘s demographic
data. IDVAR and IDVARVAL are null because the key variables STUDYID, RDOMAIN, and USUBJID are
sufficient to identify a unique parent record in DM.
suppdm.xpt: Supplemental Qualifiers for DM
Row STUDYID RDOMAIN USUBJID IDVAR IDVARVAL QNAM
QLABEL
DM
99-401
ITT
Intent to Treat
1 1996001
DM
99-401
PPROT Per Protocol Set
2 1996001

QVAL QORIG
Y DERIVED
N DERIVED

QEVAL
SPONSOR
SPONSOR

Example 2
The two rows of suppae.xpt add qualifying information to adverse event data (RDOMAIN=AE). IDVAR defines the
key variable used to link this information to the AE data (AESEQ). IDVARVAL specifies the value of the key
variable within the parent AE record that the SUPPAE record applies to. The remaining columns specify the
supplemental variables‘ names (AESOSP and AETRTEM), labels, values, origin, and who made the evaluation.
suppae.xpt: Supplemental Qualifiers for AE
Row STUDYID RDOMAIN USUBJID IDVAR IDVARVAL

QNAM

QLABEL

1996001

99-401

AESEQ

AESOSP

Other Medically
Important SAE

1996001

99-401

AESEQ

AETRTEM

Treatment Emergent
Flag

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QVAL
Spontaneous
Abortion
N

QORIG

QEVAL

CRF
DERIVED SPONSOR

CDISC SDTM Implementation Guide (Version 3.1.2)
Example 3
This is an example of how the language used for a questionnaire might be represented. The parent domain
(RDOMAIN) is QS, and IDVAR is QSCAT. QNAM holds the name of the Supplemental Qualifier variable being
defined (QSLANG). The language recorded in QVAL applies to all of the subject‘s records where IDVAR
(QSCAT) equals the value specified in IDVARVAL. In this case, IDVARVAL has values for two questionnaires
(SF36 and ADAS) for two separate subjects. QVAL identifies the questionnaire language version (French or
German) for each subject.
suppqs.xpt: Supplemental Qualifiers for QS
Row STUDYID RDOMAIN USUBJID
99-401
1 1996001 QS
99-401
2 1996001 QS
99-802
3 1996001 QS
99-802
4 1996001 QS

IDVAR IDVARVAL QNAM
QLABEL
QVAL
QSCAT
SF36
QSLANG Questionnaire Language FRENCH
QSCAT
ADAS
QSLANG Questionnaire Language FRENCH
QSCAT
SF36
QSLANG Questionnaire Language GERMAN
QSCAT
ADAS
QSLANG Questionnaire Language GERMAN

QORIG QEVAL
CRF
CRF
CRF
CRF

Example 4
The following example illustrates how data that may have been represented in an operational database as a single
domain can be expressed using an Events general-observation-class dataset and a Supplemental Qualifiers dataset.
Original Operational (non-SDTM) Dataset:

Variable

Variable Label

HOSEQ

Sequence Number

HOTERM

Term

HOSTDT

Start (Admission) Date/Time

HOENDT

End (Discharge) Date/Time

HODUR

Duration

AEREPF

AE Reported This Episode?

MEDSFL

Meds Prescribed?

PROCFL

Procedures Performed?

PROVNM

Provider Name

SPUNFL

Any Time in Spec. Unit?

SPUNCD

Specialized Unit Type

RLCNDF

Visit Related to Study Med Cond.?

Event Variables

SUPPQUAL Variables

HO Events General Observation Class Custom Dataset with SUPPHO Supplemental Qualifiers dataset:
The shading in the two datasets below is used to differentiate the three hospitalization records for which data are
shown. Note that for Rows 1-7 in the SUPPHO dataset, RDOMAIN= HO, USUBJID = 0001, IDVAR = HOSEQ,
and IDVARVAL = 1. These four values (along with STUDYID) allow these seven SUPPHO records to be linked to
the HO dataset record in Row 1 which has value in HOSEQ = 1 for Subject 0001. Likewise, SUPPHO dataset rows
8-14 are linked to the HO dataset record where HOSEQ = 2 for the same subject, and SUPPHO dataset rows 15-21
are linked to the HO dataset record where HOSEQ =1 for Subject 0002.
ho.xpt: Hospitalization ( )
Row
1
2
3

STUDYID
1999001
1999001
1999001

DOMAIN USUBJID HOSEQ HOTERM
HOSTDTC
HO
0001
1
Hospitalization 2004-01-05
HO
0001
2
Hospitalization 2004-01-23
HO
0002
1
Hospitalization 2004-01-21

HOENDTC HODUR
2004-01-12 P1W
2004-02-07 P15D
2004-01-22 P1D

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suppho.xpt: Supplemental Qualifiers for HO
Row STUDYID RDOMAIN USUBJID IDVA IDVARVAL QNAM
QLABEL
QVAL
R
HO
0001
HOSE
1
AEREPF AE Reported This Episode Y
1 1999001
Q
HO
0001
HOSE
1
MEDSFL Meds Prescribed
Y
2 1999001
Q
HO
0001
HOSE
1
PROCFL Procedures Performed
Y
3 1999001
Q
HO
0001
HOSE
1
PROVNM Provider Name
General Hosp
4 1999001
Q
HO
0001
HOSE
1
SPUNCD Specialized Unit Type
ICU
5 1999001
Q
HO
0001
HOSE
1
SPUNFL Any Time in Spec. Unit
Y
6 1999001
Q
HO
0001
HOSE
1
RLCNDF Visit Related to Study Med Y
7 1999001
Q
Cond.
HO
0001
HOSE
2
AEREPF AE Reported This Episode Y
8 1999001
Q
HO
0001
HOSE
2
MEDSFL Meds Prescribed
Y
9 1999001
Q
HO
0001
HOSE
2
PROCFL Procedures Performed
N
10 1999001
Q
HO
0001
HOSE
2
PROVNM Provider Name
Univ Hosp
11 1999001
Q
HO
0001
HOSE
2
SPUNCD Specialized Unit Type
CCU
12 1999001
Q
HO
0001
HOSE
2
SPUNFL Any Time in Spec. Unit
Y
13 1999001
Q
HO
0001
HOSE
2
RLCNDF Visit Related to Study Med Y
14 1999001
Q
Cond.
HO
0002
HOSE
1
AEREPF AE Reported This Episode Y
15 1999001
Q
HO
0002
HOSE
1
MEDSFL Meds Prescribed
N
16 1999001
Q
HO
0002
HOSE
1
PROCFL Procedures Performed
Y
17 1999001
Q
HO
0002
HOSE
1
PROVNM Provider Name
St. Mary's
18 1999001
Q
HO
0002
HOSE
1
SPUNCD Specialized Unit Type
ICU
19 1999001
Q
HO
0002
HOSE
1
SPUNFL Any Time in Spec. Unit
N
20 1999001
Q
HO
0002
HOSE
1
RLCNDF Visit Related to Study Med Y
21 1999001
Q
Cond.

QORIG QEVAL
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF
CRF

Additional examples may be found in the domain examples such as for Demographics in Examples 3, 4, and 5 under
Section 5.1.1.2, for ECGs in Example 1 under Section 6.3.1.2, and for Labs in Example 1 under Section 6.3.3.2
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8.4.4 WHEN NOT TO USE SUPPLEMENTAL QUALIFIERS
Examples of data that should not be submitted as Supplemental Qualifiers are the following:
 Subject-level objective data that fit in Subject Characteristics (SC). Examples include Subject Initials, Eye
Color.
 Findings interpretations that should be added as an additional test code and result. An example of this
would be a record for ECG interpretation where EGTESTCD = ―INTP‖, and the same EGGRPID or
EGREFID value would be assigned for all records associated with that ECG ( Section 4.1.5.5).
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


Comments related to a record or records contained within a parent dataset. Although they may have been
collected in the same record by the sponsor, comments should instead be captured in the CO specialpurpose domain.
Data not directly related to records in a parent domain. Such records should instead be captured in either a
separate general observation class or special purpose domain.

8.5 RELATING COMMENTS TO A PARENT DOMAIN
The Comments special-purpose domain, which is also described in Section 5.2, is used to capture unstructured free text
comments. It allows for the submission of comments related to a particular domain (e.g., adverse events) or those
collected on separate general-comment log-style pages not associated with a domain. Comments may be related to a
Subject, a domain for a Subject, or to specific parent records in any domain. The Comments special-purpose domain is
structured similarly to the Supplemental Qualifiers (SUPP--) dataset, in that it uses the same set of keys (STUDYID,
RDOMAIN, USUBJID, IDVAR, and IDVARVAL) to identify related records.
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All comments except those collected on log-style pages not associated with a domain are considered child records of
subject data captured in domains. STUDYID, USUBJID, and DOMAIN (with the value CO) must always be
populated. RDOMAIN, IDVAR, and IDVARVAL should be populated as follows:
1.

Comments related only to a subject in general (likely collected on a log-style CRF page/screen) would have
RDOMAIN, IDVAR, IDVARVAL null, as the only key needed to identify the relationship/association to
that subject is USUBJID.

Comments related only to a specific domain (and not to any specific record(s)) for a subject would populate
RDOMAIN with the domain code for the domain with which they are associated. IDVAR and IDVARVAL
would be null.

Comments related to specific domain record(s) for a subject would populate the RDOMAIN, IDVAR, and
IDVARVAL variables with values that identify the specific parent record(s).

If additional information is collected further describing the comment relationship to a parent record(s), and it cannot
be represented using the relationship variables, RDOMAIN, IDVAR and IDVARVAL, this can be done by two
methods:
1.

Values may be placed in COREF, such as the CRF page number or name

Timing variables may be added to the CO special-purpose domain, such as VISITNUM and/or VISIT. See
CO special-purpose Section 5.2.1.1, Assumption 6 for a complete list of Identifier and Timing variables that
can be added to the CO special-purpose domain.
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As with Supplemental Qualifiers (SUPP--) and Related Records (RELREC), --GRPID and other grouping variables
can be used as the value in IDVAR to identify comments with relationships to multiple domain records, as a
comment that applies to a group of concomitant medications, perhaps taken as a combination therapy. The limitation
on this is that a single comment may only be related to records in one domain (RDOMAIN can have only one
value). If a single comment relates to records in multiple domains the comment may need to be repeated in the CO
special-purpose domain to facilitate the understanding of the relationships.
Examples for Comments data can be found in Section 5.2.1.2.
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8.6 HOW TO DETERMINE WHERE DATA BELONG IN THE SDTM
8.6.1 GUIDELINES FOR DETERMINING THE GENERAL OBSERVATION CLASS
Section 2.6 discusses when to place data in an existing domain and how to create a new domain. A key part of the
process of creating a new domain is determining whether an observation represents an Event, Intervention, or
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Finding. Begin by considering the content of the information in the light of the definitions of the three general
observation classes (SDTM Section 2.2) rather than by trying to deduce the class from the information's physical
structure; physical structure can sometimes be misleading. For example, from a structural standpoint, one might
expect Events observations to include a start and stop date. However, Medical History data (data about previous
conditions or events) is Events data regardless of whether dates were collected.
An Intervention is something that is done to a subject (possibly by the subject) that is expected to have a
physiological effect. This concept of an intended effect makes Interventions relatively easy to recognize, although
there are grey areas around some testing procedures. For example, exercise stress tests are designed to produce and
then measure certain physiological effects. The measurements from such a testing procedure are Findings, but some
aspects of the procedure might be modeled as Interventions.
An Event is something that happens to a subject spontaneously. Most, although not all, Events data captured in
clinical trials is about medical events. Since many medical events must, by regulation, be treated as adverse events,
new Events domain will be created only for events that are clearly not adverse events; the existing Medical History
and Clinical Events domain are the appropriate places to store most medical events that are not adverse events.
Many aspects of medical events, including tests performed to evaluate them, interventions that may have caused
them, and interventions given to treat them, may be collected in clinical trials. Where to place data on assessments of
events can be particularly challenging, and is discussed further in Section 8.6.3.
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Findings general-observation-class data are measurements, tests, assessments, or examinations performed on a
subject in the clinical trial. They may be performed on the subject as a whole (e.g., height, heart rate), or on a
"specimen" taken from a subject (e.g., a blood sample, an ECG tracing, a tissue sample). Sometimes the relationship
between a subject and a finding is less direct; a finding may be about an event that happened to the subject or an
intervention they received. Findings about Events and Interventions are discussed further in Section 8.6.3.
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8.6.2 GUIDELINES FOR FORMING NEW DOMAINS
It may not always be clear whether a set of data represents one topic or more than one topic, and thus whether it
should be combined into one dataset (domain) or split into two or more datasets (domains). This implementation
guide shows examples of both.
In some cases, a single data structure works well for a variety of types of data. For example, all questionnaire data is
placed in the QS domain, with particular questionnaires identified by QSCAT ( Section 6.3.5). Although some
operational databases may store urinalysis data in a separate dataset, SDTM places all lab data is in the LB domain
(Section 6.3.3) with urinalysis tests identified using LBSPEC.
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In other cases, a particular topic may be very broad and/or require more than one data structure (and therefore
require more than one dataset). Two examples in this implementation guide are the topics of microbiology and
pharmacokinetics. Both have been modeled using two domain datasets (see Section 6.3.9 for Microbiology) and
Section 6.3.10 for Pharmacokinetics). This is because, within these scientific areas, there is more than one topic, and
each topic results in a different data structure. For example, the topic for PC is plasma (or other specimen) drug
concentration as a function of time, and the structure is one record per analyte per time point per reference time
point (e.g., dosing event) subject. PP contains characteristics of the time-concentration curve such as AUC, Cmax,
Tmax, half-life, and elimination rate constant; the structure is one record per parameter per analyte per reference
time point per subject.
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8.6.3 GUIDELINES FOR DIFFERENTIATING BETWEEN EVENTS, FINDINGS, AND
FINDINGS ABOUT EVENTS
This section discusses Events, Findings, and Findings about Events. The relationship between Interventions,
Findings, and Findings about Interventions would be handled similarly.
The Findings About domain was specially created to store findings about events. This section discusses Events and
Findings generally, but it is particularly useful for understanding the distinction between the CE and FA domains.

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There may be several sources of confusion about whether a particular piece of data belongs in an Event record or a
Findings record. One generally thinks of an event as something that happens spontaneously, and has a beginning and
end; however, one should consider the following:







Events of interest in a particular trial may be pre-specified, rather than collected as free text.
Some events may be so long lasting in that they are perceived as "conditions" rather than "events", and
their beginning and end dates are not of interest.
Some variables or data items one generally expects to see in an Events record may not be present. For
example, a post-marketing study might collect the occurrence of certain adverse events, but no dates.
Properties of an Event may be measured or assessed, and these are then treated as Findings About Events,
rather than as Events.
Some assessments of events (e.g., severity, relationship to study treatment) have been built into the SDTM
Events model as Qualifiers, rather than being treated as Findings About Events.
Sponsors may choose how they define an Event. For example, adverse event data may be submitted using
one record that summarizes an event from beginning to end, or using one record for each change in
severity.

The structure of the data being considered, although not definitive, will often help determine whether the data
represent an Event or a Finding. The questions below may assist sponsors in deciding where data should be placed
in SDTM.
Question
Is this a measurement, with
units, etc.?
Is this data collected in a CRF
for each visit, or an overall
CRF log-form?







What date/times are
collected?





Is verbatim text collected, and 
then coded?



If this is data about an event,
does it apply to the event as a
whole?




Interpretation of Answers
―Yes‖ answer indicates a Finding.
―No‖ answer is inconclusive.
Collection forms that are independent of visits suggest Event or
Intervention general observation class data
Data collected at visits is usually for items that can be controlled by the
study schedule, namely planned Findings or planned (study) Interventions
or Events.
Data collected at an initial visit may fall into any of the three general
observation classes.
If the dates collected are start and/or end dates, then data are probably
about an Event or Intervention.
If the dates collected are dates of assessments, then data probably
represents a Finding.
If dates of collection are different from other dates collected, it suggests
that data are historical, or that it is about an Event or Intervention that
happened independently of the study schedule for data collection.
―Yes‖ answer suggests that this is Events or Interventions generalobservation-class data. However, Findings general-observation-class data
from an examination that identifies abnormalities may also be coded. Note
that for Events and Interventions general-observation-class data, the topic
variable is coded, while for Findings general-observation-class data, it is
the result that is coded.
A ―No‖ answer is inconclusive. It does not rule out Events or Interventions
general-observation-class data, particularly if Events or Interventions are
pre-specified; it also does not rule out Findings general observation class
data.
―Yes‖ answer suggests this is traditional Events general-observation-class
data, and should have a record in an Events domain.
―No‖ answer suggests that there are multiple time-based findings about an
event, and that this data should be treated as Findings About data.

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The Events general observation class is intended for observations about a clinical event as a whole. Such
observations typically include what the condition was, captured in --TERM (the topic variable), and when it
happened (captured in its start and/or end dates). Other qualifier values collected (severity, seriousness, etc.) apply to
the totality of the event. Note that sponsors may choose how they define the "event as a whole."
Data that does not describe the event as a whole should not be stored in the record for that event or in a --SUPP
record tied to that event. If there are multiple assessments of an event, then each should be stored in a separate FA
record.
When data related to an event does not fit into one of the existing Event general observation class Qualifiers, the
first question to consider is whether the data represents information about the event itself, or whether it represents
data about something (a Finding or Intervention) that is associated with the event.
 If the data consist of a finding or intervention that is associated with the event, it is likely that it can be
stored in a relevant Findings or Intervention general observation class dataset, with the connection to the
Event record being captured using RELREC. For example, if a subject had a fever of 102 that was treated
with aspirin, the fever would be stored in an adverse event record, the temperature could be stored in a vital
signs record, and the aspirin could be stored in a concomitant medication record, and RELREC might be
used to link those records.
 If the data item contains information about the event, then consider storing it as a Supplemental Qualifier.
However, a number of circumstances may rule out the use of a Supplemental Qualifier:
 The data are measurements that need units, normal ranges, etc.
 The data are about the non-occurrence or non-evaluation of a pre-specified Adverse Event, data that
may not be stored in the AE domain, since each record in the AE domain must represent a
reportable event that occurred.
If a Supplemental Qualifier is not appropriate, the data may be stored in Findings About. Section 6.4
provides additional information and examples.
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Appendices
APPENDIX A: CDISC SDS TEAM *
Name
Fred Wood, Team Leader
Wayne Kubick, Past Team Lead
Barrie Nelson, SDS Leadership Team
Diane Wold, SDS Leadership Team
Karen Alexander
Randall Austin
Gary Cunningham
Dan Godoy
Andreas Gromen
Tom Guinter
Susan Hamilton
Joyce Hernandez
Jan Hess
Sandy Lei
Mary Lenzen
Richard Lewis
Tang Li
Musa Nsereko
Cliff Reinhardt
Janet Reich
Gail Stoner
Chris Tolk
Madhavi Vemuri
Gary Walker
Carolyn Wilson
Aileen Yam

Company
Octagon Research Solutions, Inc.
Lincoln Technologies
Amgen
GlaxoSmithKline
Boehringer-Ingelheim
GlaxoSmithKline
Cephalon
Astra Zeneca
Bayer Healthcare
Independent
Lilly
Merck
Procter & Gamble Pharmaceuticals
Johnson and Johnson PRD
Octagon Research Solutions, Inc
Octagon Research Solutions, Inc
Cephalon
Shire Pharmaceuticals
Schwarz Biosciences, Inc.
Take Solutions
Centocor
CDISC
Johnson and Johnson PRD
Quintiles
Forest Research Institute
Sanofi-Aventis

Jay Levine

FDA Liaison

* Individuals having met membership criteria as of publication date.

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APPENDIX B: GLOSSARY AND ABBREVIATIONS
The following abbreviations and terms are used in this document. Additional definitions can be found in the CDISC
Glossary available at http://www.cdisc.org/glossary/index.html.
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ADaM
ATC code
CDISC
CRF
CRT
CTCAE
Dataset
Domain
eDT
FDA
HL7
ICD9
ICH
ICH E2A
ICH E2B
ICH E3
ICH E9
ISO
ISO 8601
LOINC
MedDRA
NCI
SDS
SDTM
SDTMIG
SEND
SF-36
SNOMED
SOC
TDM
UUID
V3.x
WHODRUG
XML

CDISC Analysis Dataset Model
Anatomic Therapeutic Chemical code from WHO Drug
Clinical Data Interchange Standards Consortium
Case report form (sometimes case record form)
Case report tabulation
Common Terminology Criteria for Adverse Events
A collection of structured data in a single file
A collection of observations with a topic-specific commonality
Electronic Data Transfer
Food and drug Administration
Health Level 7
International Classification of Diseases, 9th revision.
International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals
for Human Use
ICH guidelines on Clinical Safety Data Management: Definitions and Standards for Expedited Reporting
ICH guidelines on Clinical Safety Data Management: Data Elements for Transmission of Individual Cases
Safety Reports
ICH guidelines on Structure and Content of Clinical Study Reports
ICH guidelines on Statistical Principles for Clinical Trials
International Organization for Standardization
ISO character representation of dates, date/times, intervals, and durations of time. The SDTM uses the
extended format.
Logical Observation, Identifiers, Names, and Codes
Medical Dictionary for Regulatory Activities
National Cancer Institute (NIH)
Submission Data Standards. Also the name of the Team that created the SDTM and SDTMIG.
Study Data Tabulation Model
Submission Data Standards Study Data Tabulation Model Implementation Guide: Human Clinical Trials [this
document]
Standard for Exchange of Non-Clinical Data
A multi-purpose, short-form health survey with 36 questions
Systematized Nomenclature of Medicine (a dictionary)
System Organ Class (from MedDRA)
Trial Design Model
Universally Unique Identifier
Version 3.1 of the SDTMIG and all subsequent versions of the SDTMIG
World Health Organization Drug Dictionary
eXtensible Markup Language

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APPENDIX C: CONTROLLED TERMINOLOGY
The current list of controlled terminology (Appendix C1) is located on the CDISC website at http://www.cancer.gov/cancertopics/terminologyresources/CDISC .
Please note that Domain Codes are also listed in Appendix C2, and Trial Summary Codes are listed in Appendix C3.
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APPENDIX C1: CONTROLLED TERMS OR FORMAT FOR SDTM VARIABLES (SEE ALSO APPENDIX C3: TRIAL
SUMMARY CODES)
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Codelist Short
Name
ACN

Description

SDTM Variable(s)

Action Taken with Study
Treatment

--ACN

AGEU

Age Unit

AGEU

AESEV

Severity/Intensity Scale
for Adverse Events

AESEV

COUNTRY

Country

COUNTRY

DSCAT

Category for Disposition
Event

DSCAT

DOMAIN
EGMETHOD

Domain Abbreviation
ECG Test Method

DOMAIN
EGMETHOD

EGSTRESC

ECG Result

EGSTRESC

EGTEST

ECG Test Name

EGTEST

Comments
Populated using a code value in the list of controlled terms, codelist ACN
(C66767) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
AGEU (C66781) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
AESEV (C66769)
athttp://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
COUNTRY (C66786) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
DSCAT (C74558) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Please see Appendix C2.
Populated using a code value in the list of controlled terms, codelist
EGMETHOD (C71151) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
EGSTRESC (C71150) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
EGTEST (C71152) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See also EGTESTCD
Populated using a code value in the list of controlled terms, codelist
EGTESTCD (C71153) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See also EGTEST
1438H

1439H

140H

14H

142H

EGTESTCD

ECG Test Code

EGTESTCD
143H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Codelist Short
Name
ETHNIC

Patient Ethnic Group

ETHNIC

FREQ

Frequency

--FRQ

FRM

Pharmaceutical Dosage
Form

--DOSFRM

IECAT

Category for
Inclusion/Exclusion

IECAT

LBTEST

Laboratory Test Name

LBTEST

Description

SDTM Variable(s)

Comments
Will be changed to Subject Ethnic Group in the future. Populated using a
code value in the list of controlled terms, codelist ETHNIC (C66790) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
FREQ (C71113) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
FRM (C66726) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
IECAT (C66797) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
LBTEST (C67154) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See also LBTESTCD
Populated using a code value in the list of controlled terms, codelist
LBTESTCD (C65047) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See also LBTEST
Populated using a code value in the list of controlled terms, codelist LOC
(C74456) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
NCOMPLT (C66727) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist ND
(C66789) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist NY
(C66742) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
14H

145H

146H

147H

148H

LBTESTCD

Laboratory Test Code

LBTESTCD
149H

LOC

Anatomical Location

--LOC

NCOMPLT

Completion/Reason for
Non-Completion

DSDECOD when DSCAT = ―DISPOSITION
EVENT‖

Not Done

--STAT

No Yes Response

1450H

145H

1452H

OUT

Outcome of Event

IEORRES, IESTRESC, --OCCUR, --PRESP, --SER
--SCAN, --SCONG, --SDISAB, --SDTH, --SHOSP,
--SLIFE, --SOD, --SMIE, --CONTRT, --BLFL,
--FAST, --DRVFL
--OUT

POSITION

Position

--POS

RACE

1453H

Populated using a code value in the list of controlled terms, codelist OUT
(C66768) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
POSITION (C71148) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
RACE (C74457) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
145H

145H

1456H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Codelist Short
Name
ROUTE

Description

SDTM Variable(s)

Route of Administration

--ROUTE

SCCD

Subject Characteristic
Code

SCTESTCD

SEX

Sex

SEX

SIZE

Size

Controlled Terms for when VSTESTCD =
FRMSIZE (Frame Size)

SOC

CDISC System Organ
Class

Could be used for --BODSYS variables but not
required to be used.

STENRF

Relation to Reference
Period

--STRF, --ENRF
See Section 4.1.4.7 ―Use of RELATIVE Timing
Variables --STRF, --STTPT, --STRTPT, --ENRF,
--ENTPT, and --ENRTPT‖ for specific regarding
controlled terminology for these variables.
Could be used for AETOXGR but not required to be
used.

Comments
Populated using a code value in the list of controlled terms, codelist
ROUTE (C66729) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
SCCD (C74559) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist SEX
(C66731) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist SIZE
(C66733) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist SOC
(C66783) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
STENRF (C66728) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
1457H

1458H

1459H

1460H

146H

TOXGR

UNIT

Common Terminology
Criteria for Adverse
Events
Unit

1462H

1463H

Populated using a code value in the list of controlled terms, codelist
TOXGR (C66784) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
UNIT (C71620) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
VSRESU (C66770) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Populated using a code value in the list of controlled terms, codelist
VSTEST (C67153) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See also VSTESTCD
Populated using a code value in the list of controlled terms, codelist
VSTESTCD (C66741) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See also VSTEST
146H

--DOSU, --ORRESU, --STRESU
1465H

VSRESU

Units for Vital Signs
Results

VSORRESU, VSSTRESU

VSTEST

Vital Signs Test Name

VSTEST

146H

1467H

VSTESTCD

Vital Signs Test Code

VSTESTCD
1468H

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CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX C2: RESERVED DOMAIN CODES
The following domain codes have been reserved for use with the domain topics listed. CDISC will be preparing additional domain models to describe many of
these over time.
Code
Domain
Class
AD Analysis Datasets Not
Applicable

Description
Added as a ―restricted prefix‖ and variable naming prefix - see
Appendix D. Do not use as a Domain Code.

Status
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Adverse Events
Events
See Section 6.2.1.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Clinical Events
Events
See Section 6.2.5.1, Assumption 1
Will be added to list of controlled terms on CDISC website.
Concomitant
Interventions See Section 6.1.1.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Medications
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Comments
Special
See Section 5.2.1.1.
Included as a value in the list of controlled terms, codelist Domain
Purpose
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Drug
Findings
Data regarding the accountability of study drug, such as
Included as a value in the list of controlled terms, codelist Domain
Accountability
information on the receipt, dispensing, return, and packaging. See Abbreviation (C66734) at
Section 6.3.8.1, Assumption 1.
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Demographics
Special
Demographics includes a set of essential standard variables that Included as a value in the list of controlled terms, codelist Domain
Purpose
describe each subject in a clinical study. It is the parent domain Abbreviation (C66734) at
for all other observations for human clinical subjects. See SDTM http://www.cancer.gov/cancertopics/terminologyresources/CDISC
2.2.6.
Disposition
Events
See Section 6.2.2.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Protocol
Events
See Section 6.2.4.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Deviations
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Electrocardiogram Findings
See Section 6.3.1.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Test Results
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Exposure
Interventions See Section 6.1.2.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Findings About
Findings
See Section 6.4.5, Assumption 1.
Will be added to list of controlled terms on CDISC website.
Inclusion/
Findings
See Section 6.3.2.1, Assumption 1.
Included as a value in the list of controlled terms, codelist Domain
Exclusion
Abbreviation (C66734) at
Criterion not met
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
201H

1469H

1470H

147H

CE
CM

1472H

1473H

147H

1475H

1476H

147H

1478H

1479H

1480H

148H

1482H

1483H

148H

1485H

1486H

1487H

FA
IE

148H

1489H

1490H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Code
Domain
LB
Laboratory Data

Class
Findings

Description
See Section 6.3.3.1, Assumption 1. Does not include
microbiology or PK data, which are stored in separate domains.

Status
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Microbiology
Findings
Microbiology Specimen findings, including gram stain results,
Included as a value in the list of controlled terms, codelist Domain
Specimen
and organisms found. See Section 6.3.9.2, Assumption 1.
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Medical History Events
See Section 6.2.3.1, Assumption 1
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Microbiology
Findings
Microbiology Susceptibility Test results, plus results of any other Included as a value in the list of controlled terms, codelist Domain
Susceptibility Test
organism-related tests. See Section 6.3.9.3, Assumption 1.
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Pharmacokinetic Findings
Concentrations of drugs/metabolites in fluids or tissues as a
Included as a value in the list of controlled terms, codelist Domain
Concentration
function of time.
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Physical
Findings
See Section 6.3.4.1, Assumption 1. Does not include vital signs Included as a value in the list of controlled terms, codelist Domain
Examination
measurements, which are stored in the VS domain.
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Pharmacokinetic Findings
Pharmacokinetic parameters derived from pharmacokinetic
Included as a value in the list of controlled terms, codelist Domain
Parameters
concentration-time (PC) data.
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Questionnaires
Findings
See Section 6.3.5.1, Assumption 1
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Subject
Findings
See Section 6.3.6.1 Assumption 1
Included as a value in the list of controlled terms, codelist Domain
Characteristics
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Subject Elements Special
See Section 5.3.1
Included as a value in the list of controlled terms, codelist Domain
Purpose
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Substance Use
Interventions See Section 6.1.3.1, Assumption 1
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Subject Visits
Special
See Section 5.3.2
Included as a value in the list of controlled terms, codelist Domain
Purpose
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Trial Arms
Trial Design See Section 7.2.1
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Trial Elements
Trial Design See Section 7.3.1
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
149H

1492H

1493H

149H

1495H

1496H

1497H

1498H

149H

150H

1502H

1503H

1504H

150H

1506H

1507H

1508H

1509H

150H

15H

152H

153H

154H

15H

156H

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CDISC SDTM Implementation Guide (Version 3.1.2)
Code
Domain
Class
TI
Trial Inclusion/
Trial Design See Section 7.5.1
Exclusion Criteria

Description

Status
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

157H

158H

Trial Summary

Trial Design See Section 7.6.1

Trial Visits

Trial Design See Section 7.4.1

Vital Signs

Findings

X-

Sponsor Defined

Y-

Sponsor Defined

Z-

Sponsor Defined

Sponsor
defined
Sponsor
defined
Sponsor
defined

159H

1520H

152H

See Section 6.3.7.1, Assumption 1
1523H

1524H

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November 12, 2008

Reserved for sponsor use; will not be used with SDTM standard
domains. The hyphen may be replaced by any letter or number.

CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX C2A: RESERVED DOMAIN CODES UNDER DISCUSSION
Code Domain

Class

Description

Status

Bone Measurements

Findings

Findings resulting from evaluations of bone.

Hospitalization

Events

Description of Hospitalization events involving research
subjects.

Healthcare Resource
Utilization

Findings

Healthcare resource utilization data such as subject visits to
physicians, hospitalizations, and nursing home stays.

Meal Data

Interventions

Information regarding the subject's meal consumption, such
as fluid intake, amounts, form (solid or liquid state),
frequency, etc., typically used for PK analysis.

Non Subject Events

Events

The description of the domain code will be corrected to Bone
Measurements. Bone Measurements is not under development.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
See HU (Healthcare Resource Utilization). Hospitalization events
(HO) will be consolidated with Healthcare Resource Utilization
(HU). The HO domain prefix will be reserved as long as HU is not
developed.
Hospitalization events (HO) will be consolidated with Healthcare
resource utilization (HU). HU is not under development.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Meal Data (ML) is not under development.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Non Subject Events (NE) is under development.

Organ Measurements

Findings

Pharmacogenomics

Findings

PH
PF

Pathology/Histology
Pharmacogenomics
Findings

Findings
Findings

Used if information is collected on anyone other than the
subject during the trial. It is not limited to just historical data
(before the study). An example of non-subject-event data is
Serious Adverse Events (SAEs) of children born to mothers
participating in the study.
Findings from organ measurement evaluations.
Organ Measurements is not under development, but under
discussion. Included as a value in the list of controlled terms,
codelist Domain Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Pharmacogenomics findings initially focusing on Genotype Pharmacogenomics (PG) is under development.
and Gene Expression data.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734)) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Findings from pathology/histology analysis
Pathology/Histology (PH) is under development.
Findings from genetic testing
Pharmacogenomics Findings (PF) is under development.

152H

1526H

1527H

1528H

1529H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)
Code Domain

Class

Description

Status

Surgery

To be
determined

Sleep Data

Findings

TR
TU

Tumor Results
Tumor Identification

Findings
Findings

Surgery (SG) might be consolidated with Procedure domain(s).
SG or Procedure domain(s) are not under development, but under
discussion.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Findings from diagnostic sleep tests (e.g., polysomnography). Sleep Data (SL) is not under development, but under discussion.
Included as a value in the list of controlled terms, codelist Domain
Abbreviation (C66734) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Results and measurements of tumors.
Tumor Results (TR) is developed and in the public review cycle.
Identification of tumors.
Tumor Identification (TU) is developed and in the public review.
1530H

153H

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November 12, 2008

CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX C3: TRIAL SUMMARY CODES
Parameters for naming the dictionaries used for a clinical trial (AEDICT, DRUGDICT, MHDICT) were developed, but we now recommend that information on
dictionaries and dictionary versions be included in the SDTM metadata, since the define.xml specification has explicit mechanisms for handling references to
dictionaries and dictionary versions. This recommendation is also based on the fact that Trial Summary is intended to convey information about the planned trial,
while dictionary use, and in particular dictionary version, may not be prospectively defined.
TSPARMCD TSPARM
ADDON
Added on to
Existing
Treatments

TSVAL

Assumptions

Status

Populated using a code value from the list of
controlled terms, codelist No Yes Response
(C66742) at
http://www.cancer.gov/cancertopics/terminology
resources/CDISC

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
153H

1532H

AEDICT

AGEMAX

AGEMIN

AGESPAN

Adverse Event Not applicable
Dictionary

Planned
No controlled terminology.
Maximum Age
of Subjects
Planned
No controlled terminology.
Minimum Age
of Subjects
Populated using a code value from the list of
Age Group
controlled terms, codelist AGESPAN (C66780)
at

The TSPARMCD code will be removed as a value from the list of
controlled terms, codelist Trial Summary Parameter Test Code
(C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
1534H

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
153H

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
1536H

A record for each applicable Included as a value in the list of controlled terms, codelist Trial
category should be included. Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
1538H

http://www.cancer.gov/cancertopics/termino
logyresources/CDISC
1537H

AGEU

Populated using a code value from the list of
Units are associated with
controlled terms, codelist AGEU (C66781) at
both AGEMIN and
http://www.cancer.gov/cancertopics/terminology AGEMAX
resources/CDISC

Age Unit
1539H

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
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CDISC SDTM Implementation Guide (Version 3.1.2)
TSPARMCD TSPARM
TSVAL
COMPTRT Comparative No controlled terminology.
Treatment
Name
DOSE
Dose per
No controlled terminology.
Administration

DOSFRQ

Assumptions

Status
In the future, may be added to list of controlled terms on
CDISC website.
Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

No controlled terminology.

In the future, may be added to list of controlled terms on
CDISC website

Dosing
Frequency

The dose associated with a
test product or comparative
treatment. Records for
dosing parameters may be
grouped using TSGRPID. In
trials with complex dosing,
it may not be useful to
submit dosing parameters, as
the TE and TA datasets are
better suited to describing
such information.
Populated using a code value in the list of
Dose frequency associated
controlled terms, codelist FREQ (C71113) at
with a test product or
http://www.cancer.gov/cancertopics/terminology comparative treatment.
resources/CDISC
Populated using a code value in the list of
Units used with value(s) in
controlled terms, codelist UNIT (C71620) at
DOSE.
http://www.cancer.gov/cancertopics/terminology
resources/CDISC
DO NOT USE Not applicable
Information on dictionaries
and dictionary versions
should be included in the
SDTM metadata, since the
define.xml specification has
explicit mechanisms for
handling references to
dictionaries and dictionary
versions.
154H

In the future, may be added to list of controlled terms on
CDISC website

1542H

DOSU

Dose Units

In the future, may be added to list of controlled terms on
CDISC website

1543H

DRUGDICT Drug
Dictionary

INDIC
LENGTH

Trial
Indication
Trial Length

Defined as the planned
Included as a value in the list of controlled terms, codelist Trial
length of time for a subject's Summary Parameter Test Code (C66738) at
participation. It should be
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
recorded using the ISO8601
format for durations, see
Section 4.1.4.3.

No controlled terminology.

1546H

154H

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CDISC SDTM Implementation Guide (Version 3.1.2)
TSPARMCD TSPARM
MHDICT
Medical
History
Dictionary

TSVAL
Not applicable

OBJPRIM

Trial Primary No controlled terminology
Objective

OBJSEC

Trial
Secondary
Objective
Planned
Number of
Subjects
Trial is
Randomized

PLANSUB

RANDOM

No controlled terminology

Assumptions

Status

DO NOT USE Information on dictionaries
and dictionary versions
should be included in the
SDTM metadata, since the
define.xml specification has
explicit mechanisms for
handling references to
dictionaries and dictionary
versions.
Should be described in terms
of the desired statement in
labeling.
Should be described in terms
of the desired statement in
labeling.

In the future, may be added to list of controlled terms on
CDISC website
In the future, may be added to list of controlled terms on
CDISC website
Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

No controlled terminology.
1548H

Populated using a code value from the list of
controlled terms, codelist NY (C66742) at
http://www.cancer.gov/cancertopics/terminology
resources/CDISC
Populated using a code value from the list of
The route associated with a
Route of
Administration controlled terms, codelist ROUTE (C66729) at test product or comparative
http://www.cancer.gov/cancertopics/terminology treatment.
resources/CDISC
Populated using a code value from the list of
Sex of
controlled terms, codelist SEXPOP (C66732) at
Participants
http://www.cancer.gov/cancertopics/terminology
resources/CDISC

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

Sponsoring
Organization
Study Stop
Rules

In the future, may be added to list of controlled terms on
CDISC website

1549H

ROUTE

150H

15H

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

SEXPOP

152H

153H

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

SPONSOR
STOPRULE

No controlled terminology.

154H

If the trial has study stop
Included as a value in the list of controlled terms, codelist Trial
rules (STOPRULE is not
Summary Parameter Test Code (C66738) at
equal to "NONE"), contains http://www.cancer.gov/cancertopics/terminologyresources/CDISC
a description of the stop
rules.
Included as a value in the list of controlled terms, codelist Trial
Trial Blinding Populated using a code value from the list of
controlled
terms,
codelist
TBLIND
(C66735)
at
Summary Parameter Test Code (C66738) at
Schema
http://www.cancer.gov/cancertopics/terminology
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
resources/CDISC
15H

TBLIND

156H

157H

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CDISC SDTM Implementation Guide (Version 3.1.2)
TSPARMCD TSPARM
TCNTRL
Control Type

TSVAL

Assumptions

Status

Populated using a code value from the list of
controlled terms, codelist TCNTRL (C66785) at
http://www.cancer.gov/cancertopics/terminology
resources/CDISC
Populated using a code value from the list of
controlled terms, codelist TDIGRP (C66787) at
http://www.cancer.gov/cancertopics/terminology
resources/CDISC

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

158H

TDIGRP

159H

If trial does not enroll
healthy subjects (TDIGRP is
not equal to "HEALTHY
SUBJECTS"), contains the
diagnosis of subjects to be
enrolled.
Populated using a code value from the list of
TINDTP provides a
controlled terms, codelist TINDTP (C66736) at classification system for the
http://www.cancer.gov/cancertopics/terminology indication provided as text in
resources/CDISC
INDIC. MITIGATION is
used narrowly to mean
mitigate the adverse effect of
another treatment.

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

No controlled terminology.

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

Diagnosis
Group
1560H

TINDTP

Trial
Indication
Type

156H

1562H

Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC

TITLE

Trial Title

TPHASE

Populated using a code value from the list of
The controlled terminology
Trial Phase
Classification controlled terms, codelist TPHASE (C66737) at for phase includes several

1563H

1564H

http://www.cancer.gov/cancertopics/terminology formats as synonyms.
resources/CDISC
156H

TRT
TTYPE

Reported Name No controlled terminology.
of Test Product
Populated using a code value from the list of
Trial Type

156H

controlled terms, codelist TTYPE (C66739) at
http://www.cancer.gov/cancertopics/terminology
resources/CDISC
1567H

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In the future, may be added to list of controlled terms on
CDISC website
Included as a value in the list of controlled terms, codelist Trial
Summary Parameter Test Code (C66738) at
http://www.cancer.gov/cancertopics/terminologyresources/CDISC
1568H

CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX C4: DRUG ACCOUNTABILITY TEST CODES
The following table contains the test codes suggested by CDISC for use in DRUG Accountability domains.
DATESTCD
DISPAMT
RETAMT

DATEST
Dispensed Amount
Returned Amount

APPENDIX C5: SUPPLEMENTAL QUALIFIERS NAME CODES
The following table contains an initial set of standard name codes for use in the Supplemental Qualifiers (SUPP--)
special-purpose datasets. There are no specific conventions for naming QNAM and some sponsors may choose to
include the 2-character domain in the QNAM variable name. Note that the 2-character domain code is not required
in QNAM since it is present in the variable RDOMAIN in the SUPP-- datasets.
QNAM
AESOSP
AETRTEM
--CLSIG
COMPLT
FULLSET
ITT
PPROT
SAFETY
--REAS
--HLGT
--HLT
--LLT
--LLTCD
--PTCD
--HLTCD
--HLGTCD
--SOCCD

QLABEL
Other Medically Important SAE
Treatment Emergent Flag
Clinically Significant
Completers Population Flag
Full Analysis Set Flag
Intent to Treat Population Flag
Per Protocol Set Flag
Safety Population Flag
Reason
High Level Group Term
High Level Term
Lowest Level Term
Lowest Level Term Code
Preferred Term Code
High Level Term Code
High Level Group Term Code
System Organ Class Code

Applicable Domains
AE
AE
Findings
DM
DM
DM
DM
DM
All general observation classes
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA
AE, MH, PE, and any other domain coded to MedDRA

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CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX D: CDISC VARIABLE-NAMING FRAGMENTS
The CDISC SDS group has defined a standard list of fragments to use as a guide when naming variables in SUPP-datasets (as QNAM) or assigning --TESTCD values that could conceivably be treated as variables in a horizontal
listing derived from a V3.x dataset. In some cases, more than one fragment is used for a given keyword. This is
necessary when a shorter fragment must be used for a --TESTCD or QNAM that incorporates several keywords that
must be combined while still meeting the 8-character variable naming limit of SAS transport files. When using
fragments, the general rule is to use the fragment(s) that best conveys the meaning of the variable within the 8character limit; thus, the longer fragment should be used when space allows. If the combination of fragments still
exceeds 8 characters, a character should be dropped where most appropriate (while avoiding naming conflicts if
possible) to fit within the 8-character limit.
In other cases the same fragment may be used for more than one meaning, but these would not normally overlap for
the same variable.
Keyword(s)
ACTION
ADJUSTMENT
ANALYSIS DATASET
ASSAY
BASELINE
BIRTH
BODY
CANCER
CATEGORY
CHARACTER
CONDITION
CLASS
CLINICAL
CODE
COMMENT
CONCOMITANT
CONGENITAL
DATE TIME - CHARACTER
DAY
DEATH
DECODE
DERIVED
DESCRIPTION
DISABILITY
DOSE, DOSAGE
DURATION
ELAPSED
ELEMENT
EMERGENT
END
ETHNICITY
EXTERNAL
EVALUATOR
EVALUATION
FASTING
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November 12, 2008

Fragment
ACN
ADJ
AD
AS
BL
BRTH
BOD
CAN
CAT
C
CND
CLAS
CL
CD
COM
CON
CONG
DTC
DY
DTH
DECOD
DRV
DESC
DISAB
DOS, DOSE
DUR
EL
ET
EM
END, EN
ETHNIC
X
EVAL
EVL
FAST

Keyword(s)
FILENAME
FLAG
FORMULATION, FORM
FREQUENCY
GRADE
GROUP
UPPER LIMIT
HOSPITALIZATION
IDENTIFIER
INDICATION
INDICATOR
INTERVAL
INTERPRETATION
INVESTIGATOR
LIFE-THREATENING
LOCATION
LOINC CODE
LOWER LIMIT
MEDICALLY-IMPORTANT EVENT
NAME
NON-STUDY THERAPY
NORMAL RANGE
NOT DONE
NUMBER
NUMERIC
OBJECT
ONGOING
ORDER
ORIGIN
ORIGINAL
OTHER
OUTCOME
OVERDOSE
PARAMETER
PATTERN

Fragment
FN
FL
FRM
FRQ
GR
GRP
HI
HOSP
ID
INDC
IND
INT
INTP
INV
LIFE
LOC
LOINC
LO
MIE
NAM
NST
NR
ND
NUM
N
OBJ
ONGO
ORD
ORIG
OR
OTH, O
OUT
OD
PARM
PATT

CDISC SDTM Implementation Guide (Version 3.1.2)
Keyword(s)
POPULATION
POSITION
QUALIFIER
REASON
REFERENCE
REGIMEN
RELATED
RELATIONSHIP
RESULT
RULE
SEQUENCE
SERIOUS
SEVERITY
SIGNIFICANT
SPECIMEN
SPONSOR
STANDARD
START
STATUS
SUBCATEGORY
SUBJECT
SUPPLEMENTAL
SYSTEM
TEXT
TIME
TIME POINT
TOTAL
TOXICITY
TRANSITION
TREATMENT
UNIT
UNIQUE
UNPLANNED
VARIABLE
VALUE
VEHICLE

Fragment
POP
POS
QUAL
REAS
REF, RF
RGM
REL, R
REL
RES
RL
SEQ
S, SER
SEV
SIG
SPEC, SPC
SP
ST, STD
ST
STAT
SCAT
SUBJ
SUPP
SYS
TXT
TM
TPT
TOT
TOX
TRANS
TRT
U
U
UP
VAR
VAL
V

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CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX E: REVISION HISTORY
Changes from CDISC SDTMIG V3.1.1 to V3.1.2
Classification
Type
Minor
Addition

2.2

Minor

Deletion

3.2

Minor

Correction 4.1.1 – General Domain
Assumptions
Deletion
4.1.1.1 – Review Study Data
Tabulation and Implementation
Guide
Addition 4.1.1.2 – Relationship to
CDISC ADaM General Considerations referenced.
Analysis Datasets
Addition 4.1.1.3 – Additional Timing
General assumption for adding timing variables was expanded to
Variables
reference Section 4.1.4.8, domain assumptions and relationship
datasets.
Addition 4.1.1.4 – Order of the Variables Additional guidance specified.
Addition 4.1.1.5 – CDISC Core
Definitions clarified.
Variables
Addition 4.1.1.6 – Additional Guidance Guidance for dataset naming described; custom domain codes
on Dataset Naming
beginning with X, Y, or Z will not overlap with future CDISC
reserved codes.
Addition 4.1.1.7 – Splitting Domains
Section and examples added.
Addition 4.1.1.8 – Origin Metadata
Section added.
Addition 4.1.1.9 – Assigning Natural
Section added.
Keys in the Metadata
Addition 4.1.2.1 – Variable-Naming
Conventions for --TESTCDs , QNAMs, and labels clarified.
Conventions
Addition 4.1.2.2 – Two-Character
Two-character prefixing further explained.
Domain Identifier
Addition 4.1.2.3 – Use of ―Subject‖ and USUBJID expectations further described with an example.
USUBJID
Addition 4.1.2.5 – Convention for
Missing values for individual data items should be represented by
Missing Values
nulls and convention regarding use of --STAT and --REASND
clarified.
Addition 4.1.2.6 – Grouping Variables
Descriptions of how the following variables group data was clarified:
and Categorization
STUDYID, DOMAIN, --CAT, --SCAT, USUBJID, --GRPID and
--REFID.
Addition 4.1.2.7 – Submitting Free Text Conventions expanded and examples added.
from the CRF
Addition 4.1.2.8 – Multiple Values for a Section added.
Variable
Addition 4.1.3 – Coding and Controlled Introductory note added referencing CDISC published controlled
Terminology Assumptions
terminology.
Addition 4.1.3.1 – Types of Controlled Controlled terminology is represented one of three ways: single
Terminology
asterisk, codelist, external codelist.
Addition 4.1.3.3 – Controlled
Convention clarified regarding values to be represented in the
Terminology Values
define.xml.
Addition 4.1.3.4 – Use of Controlled
Description clarified.
Terminology and Arbitrary
Number Codes

Minor

Minor
Minor

Minor
Minor
Minor

Major
Major
Major
Minor
Minor
Minor
Minor

Major

Major
Major
Minor
Minor
Minor
Minor

Page 286
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Section

Description of change
Adds information on how Controlled Terminology (CT) is
represented.
Reference to an outdated Metadata Model document from November,
2001 is deleted.
Section title revised to General Domain Assumptions (from General
Dataset Assumptions).
Reference to the CDISC Submission Metadata Model was deleted.

CDISC SDTM Implementation Guide (Version 3.1.2)
Classification
Type
Major
Addition

Minor

Addition

Minor

Addition

Minor

Deletion

Minor

Addition

Minor

Addition

Major

Change

Minor

Addition

Major

Addition

Major

Addition

Minor

Revised

Major

Addition

Minor

Addition

Minor

Addition

Minor

Addition

Major

Addition

Major

Addition

Major

Addition

Major

Addition

Major

Change

Section
4.1.3.5 – Storing Controlled
Terminology for Synonym
Qualifier Variables
4.1.3.7 – Use of ―Yes‖ and
―No‖ Values
4.1.4 – Actual and Relative
Time Assumptions
4.1.4.1 – Formats for
Date/Time Variables
4.1.4.2 – Date/Time Precision

Description of change
Convention clarified for values of AEBODSYS, CMCLAS and
expectation to submit dictionary name and version.
Note updated to extend values for variables with NY controlled
terminology to include ―NA‖ if collected.
Introduction to Section 4.1.4 added.
References to models prior to SDTMIG v3.1.1 removed.

References to models prior to SDTMIG v3.1.1 removed and
description clarified for omitting components for intervals of
uncertainty.
4.1.4.3 – Intervals of Time and Descriptions and examples expanded.
Use of Duration for --DUR
Variables
4.1.4.3 – Removed example of A value containing ―-P‖ cannot be used with a duration, which
negative duration, -PT2H
signifies a time interval between a start and end of an event. An event
cannot end before it starts, Note that a value containing ―-P‖ can be
used for --ELTM or --EVLINT.
4.1.4.5 – Clinical Encounters
Conventions for describing clinical encounters clarified.
and Visits
4.1.4.6 – Representing
Guidance added for representing values like ‗day within element‘ and
Additional Study Days
‗day within epoch.‘
4.1.4.7 – Use of Relative
References to models prior to SDTMIG v3.1.1 removed, conventions
Timing Variables
clarified for --STRF and --ENRF and added for --STRTPT, --STTPT,
--ENRTPT and --ENTPT.
4.1.4.8 – Date and Time
Clarified description of interval collections.
Reported in a Domain Based on
Findings
4.1.4.10 – Representing Time Section added.
Points
4.1.5.1 – Original and
Descriptions and examples clarified.
Standardized Results of
Findings and Tests Not Done
4.1.5.2 – Linking of Multiple
Text updated to point to Section 8.
Observations
4.1.5.3 – Text Strings that
Descriptions and examples expanded.
Exceed the Maximum Length
for General-Observation-Class
Domain Variables
4.1.5.5 – Clinical Significance Section added.
for Findings Observation Class
Data
4.1.5.6 – Supplemental Reason Section added.
Variables
4.1.5.7 – Presence or Absence Section added.
of Pre-Specified Interventions
and Events
Table 5.1.1 Demographics
The following Timing variables are permissible and may be added as
appropriate: VISITNUM, VISIT, VISITDY. The Record Qualifier
DMXFN (External File Name) is the only additional variable that
may be added, which is adopted from the Findings general
observation class, may also be used to refer to an external file, such
as a subject narrative.
Table 5.1.1 Demographics
Role of RFSTDTC and RFENDTC changed from ―Timing‖ to
―Record Qualifier‖.

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CDISC SDTM Implementation Guide (Version 3.1.2)
Major

Correction Table 5.1.1 Demographics

Major

Change

Major
Major

Change
Table 5.1.1 Demographics
Correction Table 5.1.1 Demographics

Major

Change

Major

Addition

Minor

Deletion

Major

Addition

Major
Major
Major
Major
Major
Major

Change
Change
Change
Change
Addition
Change

Major

Addition

Minor
Major
Minor
Minor
Major
Minor
Minor

Change
Addition
Addition
Addition
Addition
Addition
Change

Major

Change

Page 288
November 12, 2008

Table 5.1.1 Demographics

Table 5.1.1.1
Demographics
Table 5.1.1 Demographics Assumptions
Table 5.1.1.1 Demographics Assumptions

Table 5.1.1.1 Demographics Assumptions
Table 5.2.1 Comments
Table 5.2.1 Comments
Table 5.2.1 Comments
Table 5.2.1 Comments
Table 5.2.1 Comments
Table 5.2.1 Comments
Table 5.2.1.1 Comments
Assumptions
Table 5.3.1 Subject Elements
Table 5.3.1 Subject Elements
Table 5.3.1 Subject Elements
Table 5.3.2 Subject Visits
Table 5.3.2 Subject Visits
Table 5.3.2 Subject Visits
Table 6.1.1 Concomitant
Medications

Table 6.1.1 Concomitant
Medications

CDISC Notes for SITEID changed from ―Unique identifier for a
study site within a submission.‖ to ―Unique identifier for a site within
a study.‖
Role of BRTHDTC changed from ―Result Qualifier‖ to ―Record
Qualifier‖.
Role of AGE changed from ―Result Qualifier‖ to ―Record Qualifier‖.
Changed variable label for AGE from ―Age in AGEU at RFSTDTC‖
to ―Age‖ to remove names of other variables in variable labels. AGE
does not have to be derived from RFSTDTC.
ARMCD is restricted to 20 characters and not 8 characters.
Added clarifications to Assumption 4 for ARM and ARMCD.
Removed Assumption 5. Justification for using SEX vs. GENDER:
Page 71 of 'Providing Regulatory Submissions in Electronic Format NDAs' (IT-3, January, 1999), available
at http://www.fda.gov/cder/guidance/2353fnl.pdf specifically lists
SEX as part of demographic data. Similarly, page 60 of 'Guidance for
Industry,
Providing Regulatory Submissions to the Center for Biologics
Evaluation in Electronic Format - Biologics Marketing Applications'
(November, 1999),available at
http://www.fda.gov/cber/guidelines.htm specifically lists SEX as part
of demographic data. SEX is used consistently in both documents
except for one instance where GENDER is used (page 30 for Table 6
which may have been from another writer). 'ICH E3: Structure and
Content of
Clinical Study Reports' (November 30, 1999) only uses SEX (not
GENDER).‖
Added Assumption #6 for submission of multiple races.
RDOMAIN role changed from ―Identifier‖ to ‖Record Qualifier‖.
IDVAR role changed from ―Identifier‖ to ‖Record Qualifier‖.
IDVARVAL role changed from ―Identifier‖ to ‖Record Qualifier‖.
COVAL role changed from ―Result Qualifier‖ to ‖Topic‖.
Added VISITNUM, VISITDY and VISIT
CODTC is after VISITDY and is now the last variable. Was after
COREF and before COVAL
Added assumption #6, which no longer restricts the addition of
Identifiers and Timing variables to Comments.
Was Table 7.3.1
TAETORD and EPOCH added
12 assumptions added
Was Table 7.3.2
Added SVSTDY and SVENDY
Added 11 assumptions.
Structure of CM domain clarified from 'One record per medication
intervention episode per subject, Tabulation' to 'One record per
recorded intervention occurrence or constant-dosing interval per
subject'
CMSTAT label changed from 'Concomitant Medication Status' to
'Completion Status' to be compliant with the SDTM

CDISC SDTM Implementation Guide (Version 3.1.2)
Minor

Change

Table 6.1.1 Concomitant
Medications
Table 6.1.1 Concomitant
Medications
Table 6.1.1 Concomitant
Medications

Major

Addition

Major

Change

Major

Addition

Minor
Minor
Major

Addition
Addition
Change

Table 6.1.1 Concomitant
Medications
Table 6.1.2 Exposure
Table 6.1.2 Exposure
Table 6.1.2 Exposure

Minor
Major

Addition
Change

Table 6.1.2 Exposure
Table 6.1.2 Exposure

Major

Change

Table 6.1.2 Exposure

Major
Major
Major

Change
Change
Addition

Table 6.1.2 Exposure
Table 6.1.2 Exposure
Table 6.1.3 Substance Use

Minor

Change

Table 6.1.3 Substance Use

Major
Major

Change
Change

Table 6.1.3 Substance Use
Table 6.1.3 Substance Use

Minor

Deletion

Table 6.1.3 Substance Use

Major

Addition

Table 6.1.3 Substance Use

Major

Addition

Table 6.2.1 Adverse Events

Major

Deletion

Table 6.2.1 Adverse Events

Major

Change

Table 6.2.1 Adverse Events

Major
Major
Major

Addition
Addition
Change

Table 6.2.1 Adverse Events
Table 6.2.1 Adverse Events
Table 6.2.1 Adverse Events
Assumption

Assumptions have been modified to more accurately reflect the intent
of the domain
Added new variable CMPRESP after CMSCAT and before
CMOCCUR.
Changed variable label for CMDOSTOT from ―Total Daily Dose
Using DOSU‖ to ―Total Daily Dose‖ to remove names of other
variables in variable labels.
Added new variables CMSTRTPT, CMSTTPT, CMENRTPT,
CMENTPT (after CMENRF).
Added permissible variables EXVAMT and EXVAMTU
Added permissible variable EPOCH
Added assumption that Exposure data is required. Other assumptions
were added and modified.
Example for submitting placebo data has been added
Changed variable label for EXDOSTOT from ―Total Daily Dose
Using DOSU‖ to ―Total Daily Dose‖ to remove names of other
variables in variable labels.
Changed variable label for EXELTM from ―Planned Elapsed Time
from Reference Pt‖ to ―Planned Elapsed Time from Time Point Ref‖
to be consistent with SDTM Table 2.2.5 and to more accurately
represent the intent of the variable.
EXDOSFRM changed from ―Required‖ to ―Expected‖.
EXSTDTC changed from ―Required‖ to ―Expected‖.
Added new variable SUPRESP after SUSCAT and before
SUOCCUR.
Structure of SU domain clarified from 'One record per substance type
per visit per subject' to 'One record per substance type per reported
occurrence per subject'
SUSTAT label changed from 'Substance Use Status' to 'Completion
Status' to be more compliant with the SDTM
Changed variable label for SUDOSTOT from ―Total Daily Dose
Using DOSU‖ to ―Total Daily Dose‖ to remove names of other
variables in variable labels.
Removed variables VISIT, VISITNUM and VISITDY but can be
added back in if needed since they are timing variables.
Added new variables SUSTRTPT, SUSTTPT, SUENRTPT,
SUENTPT (after SUENRF).
Added new variable AEPRESP after AESCAT and before
AEBODSYS
Removed variable AEOCCUR. AEOCCUR is not permitted because
the AE domain contains only records for adverse events that actually
occurred.
Changed variable label for AELOC from ―Location of the Reaction‖
to ―Location of Event‖ to be more generic and not limited to just a
location of a reaction.
Added new variables AEENRTPT, AEENTPT (after AEENRF).
Added assumption #7 to clarify use of EPOCH and TAETORD.
The adverse event dataset is only for adverse events that happened.
Assumption 4e ―Records should be included in the submission AE
dataset only for adverse events that have actually occurred.‖

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CDISC SDTM Implementation Guide (Version 3.1.2)
Major

Addition

Table 6.2.1 Adverse Events
Assumption #8

Minor

Deletion

Table 6.2.2 Disposition

Major

Change

Table 6.2.2 Disposition

Major
Minor

Change
Addition

Table 6.2.2 Disposition
Table 6.2.2 Disposition

Major
Major

Change
Addition

Table 6.2.3 Medical History
Table 6.2.3 Medical History

Minor

Deletion

Table 6.2.3 Medical History

Major
Minor

Change
Addition

Major
Minor

Addition
Deletion

Table 6.2.3 Medical History
Section 6.2.4 Protocol
Deviations
Section 6.2.5 Clinical Events
Table 6.3.1 ECG

Minor

Minor
Major

Major
Major

Minor
Major\
Major
Major
Minor
Minor
Major

The following Qualifiers would not be used in AE: --OCCUR,
--STAT, and--REASND. They are the only Qualifiers from the
SDTM Events Class not in the AE domain. They are not permitted
because the AE domain contains only records for adverse events that
actually occurred. See Assumption 4c above for information on how
to deal with negative responses or missing responses to probing
questions for pre-specified adverse events.
Removed variables VISIT, VISITNUM and VISITDY but can be
added back in if needed since they are timing variables.
EPOCH label changed from ―Trial Epoch‖ to ―Epoch‖. This change
is consistent with SDTM Table 2.2.5, which is the master for the
Timing Variables.
DSCAT changed from ―Permissible‖ to ―Expected‖.
Added assumptions #5 and #6 for ICH E3 guidance.
MHSTAT label changed from 'Medical History Status' to
'Completion Status' to be more compliant with the SDTM
Added new variable MHPRESP after MHSCAT and before
MHOCCUR.
Removed variables VISIT, VISITNUM and VISITDY but can be
added back in if needed since they are timing variables.
Added new variables MHENRTPT, MHENTPT (after MHENRF).
Added new domain model, assumptions and examples.

Added new domain model, assumptions and examples.
EGNRIND removed but can be added back if data is collected or
derived.
Deletion
Table 6.3.1 ECG
EGLOINC removed but can be added back if data is collected or
derived. EGLOINC was removed from EG because its use is no
longer recommended. Other coding schemes for EGTEST will be
proposed by the CDISC Terminology Team.
Addition Table 6.3.1 ECG
Permissible variable EGLOC added but can be dropped if data is not
collected.
Correction Table 6.3.1 ECG
Order of variables changed to VISITNUM, VISIT from VISIT,
VISITNUM. This change is consistent with SDTM Table 2.2.5,
which is the master for the Timing Variables.
Change
Table 6.3.1 ECG
VISITNUM changed from ―Required‖ to ―Expected‖.
Correction Table 6.3.1 ECG
Changed variable label for EGELTM from ―Elapsed Time from
Reference Point‖ to ―Planned Elapsed Time from Time Point Ref‖ to
be consistent with SDTM Table 2.2.5 and to more accurately
represent the intent of the variable.
Addition Table 6.3.1 ECG
Permissible variable EGRFTDTC added but can be dropped if data is
not collected.
Change
Table 6.3.1 ECG
EGSTAT label changed to be consistent across domains.
Change
Table 6.3.1 ECG
EGXFN label has the 'F' in 'file' capitalized to be title case.
Change
Table 6.3.1 ECG
EGEVAL changed from Expected to Permissible.
Change
Table 6.3.1 ECG
EGTPT moved before EGTPTNUM to be consistent with the order in
the SDTM.
Deletion
Table 6.3.1 ECG
Previous assumption #2 removed because it pertains to EGLOINC,
which has been removed from the model.
Correction Table 6.3.2 Inclusion/Exclusion Order of variables changed to VISITNUM, VISIT from VISIT,
Exceptions
VISITNUM. This change is consistent with SDTM Table 2.2.5,
which is the master for the Timing Variables.

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Table 6.3.2 Inclusion/Exclusion Structure of IE domain clarified from 'One record per
inclusion/exclusion criteria exception per subject' to 'One record per
inclusion/exclusion criterion not met per subject'
Table 6.3.2 Inclusion/Exclusion 3 assumptions added
Table 6.3.2 Inclusion/Exclusion Previous assumption #2 removed
Table 6.3.3 Lab
LBSTAT label was changed from 'Lab Status' to 'Completion Status'
in order to be more compliant with the SDTM
Table 6.3.3 Lab
VISITNUM changed from Required to Expected
Table 6.3.3 Lab
4 assumptions added
Table 6.3.3
LBTESTCD variable label changed from ―LAB Test or Examination
Laboratory Test Results
Short Name‖ to ‖Lab Test or Examination Short Name‖
Table 6.3.3
LBTESTCD variable label changed from ―LAB Test or Examination
Laboratory Test Results
Name‖ to ‖Lab Test or Examination Name‖
Table 6.3.3
Order of LBSTNRC changed from after LBSTRESC and before
Laboratory Test Results
LBSTRESN to after LBSTNRHI and before LBNRIND. This change
is consistent with SDTM Table 2.2.3, which is the master for the
Findings Observation Class.
Table 6.3.3
Order of LBDRVFL changed from after LBFAST and before
Laboratory Test Results
LBSTRESN to after LBFAST and before LBTOX. This change is
consistent with SDTM Table 2.2.3, which is the master for the
Findings Observation Class.
Table 6.3.3
Order of variables changed to VISITNUM, VISIT from VISIT,
Laboratory Test Results
VISITNUM. This change is consistent with SDTM Table 2.2.5,
which is the master for the Timing Variables.
Table 6.3.3
Changed variable label for LBELTM from ―Elapsed Time from
Laboratory Test Results
Reference Point‖ to ―Planned Elapsed Time from Time Point Ref‖ to
be consistent with SDTM Table 2.2.5 and to more accurately
represent the intent of the variable.
Table 6.3.3
Permissible variable LBRFTDTC added but can be dropped if data is
Laboratory Test Results
not collected.
Table 6.3.4 Physical
PESTRESC label had the period after "Std" removed
Examination
Table 6.3.4 Physical
Removed expected variable PESTRESN
Examination
Table 6.3.4 Physical
Removed expected variable PESTRESU
Examination
Table 6.3.4 Physical
PESTAT had the label changed from 'Examination Status' to
Examination
'Completion Status' in order to be more compliant with the SDTM
Table 6.3.4 Physical
Permissible variable PESEV was dropped from the model, but can be
Examination
added back in if collected.
Table 6.3.4 Physical
2 assumptions were added
Examination
Table 6.3.4 Physical
Order of PELOC changed from after PESCAT and before
Examination
PEBODSYS to after PEREASND and before PEMETHOD. This
change is consistent with SDTM Table 2.2.3, which is the master for
the Findings Observation Class.
Table 6.3.4 Physical
Permissible variable PEMETHOD added but can be dropped if data
Examination
is not collected.
Table 6.3.4 Physical
Removed PEBLFL.
Examination
Table 6.3.4 Physical
Order of variables changed to VISITNUM, VISIT from VISIT,
Examination
VISITNUM. This change is consistent with SDTM Table 2.2.5,
which is the master for the Timing Variables.

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Table 6.3.6 Subject
Characteristics
Table 6.3.6 Subject
Characteristics
Table 6.3.6 Subject
Characteristics
Table 6.3.6 Subject
Characteristics
Table 6.3.7 Vital Signs
Table 6.3.7 Vital Signs
Table 6.3.7 Vital Signs
Table 6.3.7 Vital Signs

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Section 6.3.8 Drug
Accountability
Section 6.3.9 Microbiology
Section 6.3.10 PK
Section 6.4 Findings About
7.1 Introduction

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Structure of QS domain clarified from 'One record per question per
time point per visit per subject' to 'One record per questionnaire per
question per time point per visit per subject'
Label of QSSTAT changes from 'Status of Question' to 'Completion
Status' in order to be more compliant with the SDTM
The first three assumptions were rearranged for clarity. 3 additional
assumptions were added.
Order of variables changed to VISITNUM, VISIT from VISIT,
VISITNUM. This change is consistent with SDTM Table 2.2.5,
which is the master for the Timing Variables.
Changed variable label for QSELTM from ―Elapsed Time from
Reference Point‖ to ―Planned Elapsed Time from Time Point Ref‖ to
be consistent with SDTM Table 2.2.5 and to more accurately
represent the intent of the variable.
Permissible variable QSRFTDTC added but can be dropped if data is
not collected.
SCSTAT label changed from 'Status of SD Measurement' to
'Completion Status' in order to be more compliant with the SDTM
1 assumption added
Example (previously in 9.4.6) had 'Race Other' information removed
SCDTC changed from ―Expected‖ to ―Permissible‖.
VISITNUM changed from Required to Expected
4 assumptions added
VISITNUM changed from Required to Expected
VSNRIND removed but can be added back if data is collected or
derived.
VSLOINC removed. CDISC had defined the controlled terminology
for Vital Signs Tests.
Order of variables changed to VISITNUM, VISIT from VISIT,
VISITNUM. This change is consistent with SDTM Table 2.2.5,
which is the master for the Timing Variables.
Changed variable label for VSELTM from ―Elapsed Time from
Reference Point‖ to ―Planned Elapsed Time from Time Point Ref‖ to
be consistent with SDTM Table 2.2.5 and to more accurately
represent the intent of the variable.
Permissible variable VSRFTDTC added but can be dropped if data is
not collected.
Added new domain model, assumptions and examples.
Added new domain model, assumptions and examples.
Added new domain model, assumptions and examples.
Added new domain model, assumptions and examples.
Expanded introduction and added subsections 7.1.1 Purpose of Trial
Design Model, 7.1.2 Definitions of Trial Design Concepts and 7.1.3
Current and Future Contents of the Trial Design Model.
ETCD is restricted to 8 characters. Length was not specified
previously.
Was Table 7.2.2
ARMCD is restricted to 20 characters and not 8 characters.
ARMCD label changed from ―Arm Code‖ to ―Planned Arm Code‖.

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Table 7.2.1 Trial Arms
Table 7.3.1 Trial Elements
Table 7.3.1 Trial Elements

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Table 7.3.1 Trial Elements
Table 7.4.1 Trial Visits
Table 7.4.1 Trial Visits
Table 7.4.1 Trial Visits
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Table 7.4.1 Trial Visits
Table 7.4.1 Trial Visits
Table 7.5.1 Trial
Inclusion/Exclusion Criteria
Table 7.5.1 Trial
Inclusion/Exclusion Criteria
Table 7.5.1 Trial
Inclusion/Exclusion Criteria
Table 7.5.1 Trial
Inclusion/Exclusion Criteria
Table 7.6.1 Trial Summary
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ARM label changed from ―Description of Arm‖ to ―Description of
Planned Arm‖.
EPOCH label changed from ―Trial Epoch‖ to ―Epoch‖. This change
is consistent with SDTM Table 2.2.5, which is the master for the
Timing Variables.
EPOCH changed from ―Permissible‖ to ―Required‖.
11 assumptions added
Was Table 7.2.1
ETCD is restricted to 8 characters. Length was not specified
previously.
Added 15 assumptions
Was Table 7.2.3
ARMCD is restricted to 20 characters and not 8 characters.
ARMCD label changed from ―Arm Code‖ to ―Planned Arm Code‖.
ARM label changed from ―Description of Arm‖ to ―Description of
Planned Arm‖.
TVSTRL changed from ―Permissible‖ to ―Required‖.
6 assumptions added
Was Table 7.9
Added new qualifier variable IESCAT to list of qualifiers (after
IECAT and before TIRL).
Added new qualifier variable TIVERS to list of qualifiers (after
TIRL).
Added 4 assumptions.

Was Table 7.10
Added new qualifier variable TSGRPID to list of qualifiers (after
TSSEQ and before TSPARMCD).
Table 7.6.1 Trial Summary
Added 10 assumptions.
Section 8 Representing
Clarified relationship description. Emphasis was placed on defining
Relationships and Data
relationships between datasets rather than domains since domains
may occupy multiple datasets.
Section 8.1 – Relating Groups Simplified wording to clarify concepts especially the use of GRPID
of Records within a Domain
to group records within a subject versus the use of the variable CAT
using the –GRPID Variable
that can group records across subjects.
Table 8.2.1 RELREC
Added columns for ―Core‖ and ―References‖.
8.3.1 RELREC Dataset
Added more explanation on the different RELTYPES and the
Relationship Example
functionality each provides.
8.4 Relating Non-Standard
Re-arranged wording to gradually introduce topics by first building
Variables to a Parent Domain the understanding of foundational concepts such as metadata and
attributions.
8.4.1 Supplemental Qualifiers: Added reference to another section for handling data that is greater
SUPPQUAL or SUPP -than 200 characters. Also added a reference to standard QNAMs for
Datasets
commonly represented data.
Table 8.4.1 SUPPQUAL
Added column for ―Core‖.
8.4.2 Submitting Supplemental Added reference to section for additional guidance on splitting
Qualifiers in Separate Datasets domains.
8.4.3 SUPPQUAL Examples
Added an example on how to use SUPPQUAL with a sponsordefined domain.
8.4.4 When not to use
New section with examples that qualify use of SUPPQUAL versus
Supplemental Qualifiers
other domains.

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8.6.1 Guidelines for
Determining the General
Observation Class
8.6.2 Guidelines for Forming
New Domain
8.6.3 Guidelines for
Differentiating Between
Events, Findings and Findings
About Events
Section 10.1 – CDISC Team

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Appendix C1

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10.3.1 – Reserved Domain
Codes – BM

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Codes – BR (Biopsy)

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10.3.1 – Reserved Domain
Codes – DC (Disease
Characteristics)
10.3.1 – Reserved Domain
Codes – EE
(Electroencephalogram)
10.3.1 – Reserved Domain
Codes – IM
10.3.1 – Reserved Domain
Codes – SK (Skin Test)

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Section 10.3.1 – Reserved
Domain Codes
Section C2A
10.3.1 – Reserved Domain
Codes
Section C2A

10.3.1 – Reserved Domain
Codes – EG, PP, and PC
10.3.1 – Reserved Domain
Codes – AU (Autopsy)

Add several paragraphs that provide guidance on how to use CO
(Comments) to store information that describes the comment
relationship.
Provides more concrete examples for each type of observation class.

New section that describes how data topics influences whether or not
to create a new domain.
New section that describes the attributes that may be used to
distinguish between Events and Findings.

Renamed to Appendix A. Updated to reflect current list of SDS team
members and company affiliation.
Renamed to Appendix B. Added terms ADaM, ATC code, CRF,
CTCAE, eDT, ICD9, ICH, ICH E2A, ICH E2B, ICH E3, ICH E9,
ISO, ISO 8601, LOINC, MedDRA, NCI, SF-36, SNOMED, SOC,
TDM ,UUID, WHODRUG, XML
Deleted SDSIG (SDS Implementation Guide V3.1, now referred to as
SDTMIG.)
New section added: ―Appendix C1: Controlled Terms or Format for
SDTM Variables‖. Replaced values for controlled terminology to
links to CDISC website.
Renamed to ―Appendix C2: Reserved Domain Codes‖
New section added: Appendix C2A: Reserved Domain Codes Under
Discussion
Added AD (Analysis Dataset), CE (Clinical Events), FA (Findings
About) and X, Y, Z (used for sponsor defined domains).
Added HO (Hospitalization), NE (Non Subject Events, PH
(Pathology/Histology), PF (Pharmacogenomics Findings), TR
(Tumor Results), TU (Tumor Identification)
Abbreviations spelled out for ECG (Electrocardiogram), PK
(Pharmacokinetic)
Information about the autopsy (AU) itself would belong in a
procedures domain. Finding obtained during the autopsy would more
likely belong in the domain based on the topic.
Bone Mineral Density changed to Bone Measurements to be more
generic since Bone Mineral Density is one type of Bone
Measurement.
Information about the biopsy (BR) itself would belong in a
procedures domain. Finding obtained during the biopsy would more
likely belong in the domain based on the topic.
Disease Characteristics are more likely to be CE (Clinical Events) or
FA (Findings About), which are new models in 3.1.2.
Domains are established based on a common topic (i.e., where the
nature of the measurements is the same), rather than by a specific
method of collection such as Electroencephalogram.
Imaging removed as a domain.
Not under development and concept is too vague for the creation of a
domain model.

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10.3.1 – Reserved Domain
Code SL (Sleep
(Polysomnography) Data)
10.3.1 – Reserved Domain
Codes – SS (Signs and
Symptoms)
10.3.1 – Reserved Domain
Codes – ST (Stress (Exercise)
Test Data.
10.3.2: Electrocardiogram Test
Codes and 10.3.3 Vital Signs
Test Codes
Controlled Terminology - Units
for Vital Signs Results
(VSRESU)

Changed ―SL (Sleep (Polysomnography) Data)‖ to ―Sleep Data‖.
Polysomnography is an example of a finding from diagnostic sleep
tests.
Replaced by Findings About.

Findings are usually in existing domains such as ECG, laboratory,
vital signs etc.

Controlled terminology is published on the CDISC website. Section
replaced by ―Appendix C1: Controlled Terms or Format for SDTM
Variables‖.
Controlled terminology is published on the CDISC website.
Examples were in CDISC notes for VSORRES. More units were
added and some were modified:
1) INCHES changed to IN
2) FEET is not included
3) POUNDS changed to LB
4) BEATS PER MINUTE changed to BEATS/MINUTE
Section 10.3.3 Vital Signs Test Controlled terminology is published on the CDISC website and some
Codes (VSTESTCD, VSTEST) changes made from10.3.3 Vital Signs Test Codes Page 167
1) VSTEST ―Frame Size‖ changed to ―Body Frame Size‖
2) VSTEST ―Body Fat‖ changed to ―Adipose Tissue‖
Controlled Terminology –
Examples were in CDISC notes for AEACN- DRUG
Action Taken with Study
INTERRUPTED was not included as an example.
Treatment
Controlled Terminology –SEX Controlled terms for SEX (Undifferentiated) added to be consistent
with HL7 (Health Level 7).
Controlled Terminology –
Additions to those listed as controlled terms for ETHNIC. ―NOT
Ethnicity
REPORTED‖ and ―UNKNOWN‖ added as terms to match what was
already in NCI caDSR (National Cancer Institute cancer Data
Standards Repository)
Controlled Terminology –
Examples were in CDISC notes for DSDECOD disposition events.
Completion/Reason for NonAdded ―RECOVERY‖, Changed ―WITHDRAWAL OF CONSENT‖
Completion (NCOMPLT)
to ―WITHDRAWAL BY SUBJECT‖
Controlled Terminology – No NA (Not Applicable) was added to the list of controlled terms N, Y,
Yes response (NY)
and U. (No, Yes and Unknown).
Controlled Terminology –
Includes many more controlled terms than for those listed in CDISC
Route of Administration
Notes for --ROUTE. Includes more specific routes than
(ROUTE)
INHALATION listed in CDISC notes for SUROUTE.
10.3.5 Trial Summary Codes
Section moved and renamed to ―Appendix C3: Trial Summary
Codes‖.
10.3.5 Trial Summary Codes
All values for TSPARM changed to title case to be consistent with
--TEST.
Controlled Terminology - Trial TSPARMCD value ADDON The TSPARM was changed from
Blinding Schema (ADDON)
―TEST PRODUCT IS ADDED ON TO EXISTING TREATMENT‖
to ―Added on to Existing Treatments‖.
Controlled Terminology - Trial AEDICT, DRUGDICT, MHDICT are no longer recommended to be
Summary Parameter (AEDICT, used as Trial Summary Parameters. Information on dictionaries and
DRUGDICT, MHDICT)
dictionary versions are included in the SDTM metadata, since the
define.xml specification has explicit mechanisms for handling
references to dictionaries and dictionary versions.
Controlled Terminology - Trial TSPARM for AGESPAN label changed from ―AGE SPAN‖ to ―Age
Phase (AGESPAN)
Group‖ to be more descriptive.

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Controlled Terminology - Trial
Phase (AGEU)
Controlled Terminology - Trial
Blinding Schema (TBLIND)
Controlled Terminology - Trial
Phase (COMPTRT)
Controlled Terminology - Trial
Control Type (TCNTRL)
Controlled Terminology Diagnosis Group (TDIGRP)
Controlled Terminology Diagnosis Group (DOSE)
Controlled Terminology Diagnosis Group (DOSFRQ)

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Controlled Terminology - Trial
Phase (DOSFRQ)
Change
Controlled Terminology Diagnosis Group (DOSU)
Change
Controlled Terminology - Trial
Phase (DOSU)
Correction Controlled Terminology - Trial
Phase (INDIC)

Change

Controlled Terminology - Trial
Phase (INDIC)

Controlled Terminology - Trial
Indication Type (TINDTP)
Correction Controlled Terminology - Trial
Phase (LENGTH)
Change
Controlled Terminology - Trial
Phase (OBJPRIM)
Change
Controlled Terminology - Trial
Phase (OBJSEC)
Change
Controlled Terminology - Trial
Phase (TPHASE)
Change
Controlled Terminology - Trial
Summary Parameter (ROUTE)
Change
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Addition

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AGEU (Age Units) added.
TSPARMCD was changed from BLIND to TBLIND.
TSPARMCD value COMPTRT (Comparative Treatment Name) has
been deferred to a later package based on review comments.
TSPARMCD was changed from CONTROL to TCNTRL.
Label changed from ―TYPE OF CONTROL‖ to ―Control Type‖
TSPARMCD was changed from DIAGGRP to TDIGRP
TSPARMCD value DOSE. The TSPARM was changed from ‖TEST
PRODUCT DOSE PER ADMINISTRATION‖ to ―Dose per
Administration‖.
TSPARMCD value DOSFRQ. The TSPARM was changed from
‖TEST PRODUCT DOSING FREQUENCY‖ to ―Dosing
Frequency‖.
TSPARMCD value DOSFRQ (Dosing Frequency) has been deferred
to a later package based on review comments.
TSPARMCD value DOSU. The TSPARM was changed from ‖TEST
PRODUCT DOSE UNITS‖ to ―Dose Units‖.
TSPARMCD value DOSU (TEST PRODUCT DOSE UNITS) has
been deferred to a later package based on review comments.
TSPARMCD value INDIC. The TSPARM was changed from
‖TRIAL INDICATIONS‖ to ―Trial Indication‖. The plural form has
not been used with trial summary parameters so change for
consistency.
TSPARMCD value INDIC (Trial Indication) has been deferred to a
later package since it‘s not apparent what the distinction is between
―Trial Indication‖ and ―Trial Indication Type‖.
TSPARMCD was changed from INDICTYP to TINDTP.

TSPARMCD value LENGTH. The TSPARM was changed from
‖LENGTH OF TRIAL‖ to ―Trial Length‖.
TSPARMCD value OBJPRIM (TRIAL PRIMARY OBJECTIVE)
has been deferred to a later package based on review comments.
TSPARMCD value OBJSEC (TRIAL SECONDARY OBJECTIVE)
has been deferred to a later package based on review comments.
TSPARMCD was changed from PHASE to TPHASE. Label changed
from ―TRIAL PHASE‖ to ―Trial Phase Classification‖
TSPARMCD value ROUTE. The TSPARM was changed from
‖TEST PRODUCT ROUTE OF ADMINISTRATION‖ to ―Route of
Administration‖.
Controlled Terminology - Trial TSPARMCD value SPONSOR (SPONSORING ORGANIZATION)
Phase (SPONSOR)
has been deferred to a later package.
Controlled Terminology - Trial TSPARMCD value TRT (REPORTED NAME OF TEST
Phase (TRT)
PRODUCT) has been deferred to a later package.
Appendix C3: Trial Summary STOPRULE (Study Stop Rules) added.
Codes

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Controlled Terminology - Trial TSPARMCD was changed from TYPE to TTYPE.
Summary Parameter (TTYPE)
Controlled Terminology - Trial TSPARMCD value TTYPE. The TSPARM was changed from
Summary Parameter (TTYPE) ‖TYPE OF TRIAL‖ to ―Trial Type‖.
Controlled Terminology - Type
 TSVAL CONFIRMATORY and EXPLORATORY were
of Trial (TTYPE)
removed based on review comments.
 TSVAL PHARMACODYNAMICS changed to
PHARMACODYNAMIC
 TSVAL PHARMACOGENOMICS changed to
PHARMACOGENOMIC
 TSVAL PHARMACOKINETICS changed to
PHARMACOKINETIC
Section 10.4 CDISC Variable- Section renamed to ―Appendix D: CDISC Variable- Naming
Naming Fragments.
Fragments‖. Added ASSAY, CLINICAL, OBJECT, SIGNIFICANT
Appendix C4: Drug
New section added and values: ―Appendix C4: Drug Accountability
Accountability Test Codes
Test Codes‖
Section 10.3.4 Supplemental
Section renamed to ―Appendix C5: Supplemental Qualifiers Name
Qualifiers Name Codes
Codes‖.
Section 10.3.4 Supplemental
Added MedDRA specific values (--HLGT = High Level Group
Qualifiers Name Codes
Term, --HLT= High Level Term, --LLT= Lowest Level Term,
--LLTCD = Lowest Level Term Code, --PTCD = Preferred Term
Code, --HLTCD = High Level Term Code, --HLGTCD = High Level
Group Term Code, --SOCCD = System Organ Class Code,)
Section 10.3.4 Supplemental
Added --CLSIG = Clinically Significant and --REAS = Reason.
Qualifiers Name Codes
Section 10.5 Lessons Learned Section 10.5 Lessons Learned from the Pilot is deleted since it is
from the Pilot
historical information and not relevant to this release.

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CDISC SDTM Implementation Guide (Version 3.1.2)

APPENDIX F: REPRESENTATIONS AND WARRANTIES, LIMITATIONS
OF LIABILITY, AND DISCLAIMERS
CDISC Patent Disclaimers
It is possible that implementation of and compliance with this standard may require use of subject matter covered by
patent rights. By publication of this standard, no position is taken with respect to the existence or validity of any
claim or of any patent rights in connection therewith. CDISC, including the CDISC Board of Directors, shall not be
responsible for identifying patent claims for which a license may be required in order to implement this standard or
for conducting inquiries into the legal validity or scope of those patents or patent claims that are brought to its
attention.
Representations and Warranties
Each Participant in the development of this standard shall be deemed to represent, warrant, and covenant, at the time
of a Contribution by such Participant (or by its Representative), that to the best of its knowledge and ability: (a) it
holds or has the right to grant all relevant licenses to any of its Contributions in all jurisdictions or territories in
which it holds relevant intellectual property rights; (b) there are no limits to the Participant‘s ability to make the
grants, acknowledgments, and agreements herein; and (c) the Contribution does not subject any Contribution, Draft
Standard, Final Standard, or implementations thereof, in whole or in part, to licensing obligations with additional
restrictions or requirements inconsistent with those set forth in this Policy, or that would require any such
Contribution, Final Standard, or implementation, in whole or in part, to be either: (i) disclosed or distributed in
source code form; (ii) licensed for the purpose of making derivative works (other than as set forth in Section 4.2 of
the CDISC Intellectual Property Policy (―the Policy‖)); or (iii) distributed at no charge, except as set forth in
Sections 3, 5.1, and 4.2 of the Policy. If a Participant has knowledge that a Contribution made by any Participant or
any other party may subject any Contribution, Draft Standard, Final Standard, or implementation, in whole or in
part, to one or more of the licensing obligations listed in Section 9.3, such Participant shall give prompt notice of the
same to the CDISC President who shall promptly notify all Participants.
No Other Warranties/Disclaimers. ALL PARTICIPANTS ACKNOWLEDGE THAT, EXCEPT AS PROVIDED
UNDER SECTION 9.3 OF THE CDISC INTELLECTUAL PROPERTY POLICY, ALL DRAFT STANDARDS
AND FINAL STANDARDS, AND ALL CONTRIBUTIONS TO FINAL STANDARDS AND DRAFT
STANDARDS, ARE PROVIDED ―AS IS‖ WITH NO WARRANTIES WHATSOEVER, WHETHER EXPRESS,
IMPLIED, STATUTORY, OR OTHERWISE, AND THE PARTICIPANTS, REPRESENTATIVES, THE CDISC
PRESIDENT, THE CDISC BOARD OF DIRECTORS, AND CDISC EXPRESSLY DISCLAIM ANY
WARRANTY OF MERCHANTABILITY, NONINFRINGEMENT, FITNESS FOR ANY PARTICULAR OR
INTENDED PURPOSE, OR ANY OTHER WARRANTY OTHERWISE ARISING OUT OF ANY PROPOSAL,
FINAL STANDARDS OR DRAFT STANDARDS, OR CONTRIBUTION.
Limitation of Liability
IN NO EVENT WILL CDISC OR ANY OF ITS CONSTITUENT PARTS (INCLUDING, BUT NOT LIMITED TO,
THE CDISC BOARD OF DIRECTORS, THE CDISC PRESIDENT, CDISC STAFF, AND CDISC MEMBERS) BE
LIABLE TO ANY OTHER PERSON OR ENTITY FOR ANY LOSS OF PROFITS, LOSS OF USE, DIRECT,
INDIRECT, INCIDENTAL, CONSEQUENTIAL, OR SPECIAL DAMAGES, WHETHER UNDER CONTRACT,
TORT, WARRANTY, OR OTHERWISE, ARISING IN ANY WAY OUT OF THIS POLICY OR ANY RELATED
AGREEMENT, WHETHER OR NOT SUCH PARTY HAD ADVANCE NOTICE OF THE POSSIBILITY OF
SUCH DAMAGES.
Note: The CDISC Intellectual Property Policy can be found at
http://www.cdisc.org/about/bylaws_pdfs/CDISCIPPolicy-FINAL.pdf.

Page 298
November 12, 2008

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Title                           : SDTMIG V3.1.2
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Page Count                      : 298
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Author                          : CDISC SDS Team

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SDTMIG V3.1.2 SDTM Implementation Guide

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