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STUDY DATA

TECHNICAL CONFORMANCE GUIDE

Technical Specifications Document

This Document is incorporated by reference into the following

Guidance Document(s):

Guidance for Industry Providing Regulatory Submissions in Electronic

Format – Standardized Study Data

For questions regarding this technical specifications document, contact CDER at

cder-edata@fda.hhs.gov or CBER at cber.cdisc@fda.hhs.gov

U.S. Department of Health and Human Services

Food and Drug Administration

Center for Drug Evaluation and Research (CDER)

Center for Biologics Evaluation and Research (CBER)

November 2016

STUDY DATA

TECHNICAL CONFORMANCE GUIDE

November 2016

Revision History

Date

Versi

Summary of Revisions

January 2014

1.0

Initial Version

December 2014

2.0

Revisions based on the public comment period (February 2014 – May 2014); and CDER/CBER

internal review May 2014 – December 2014

March 2015

2.1

Revisions based on comments received to version 2.0. Updates to Sections 2.2 Study Data

Reviewer’s Guide (SDRG)

SDRG, 2.3 Analysis Data Reviewer’s (ADRG), 3.3.1 SAS Transport Format, 3.3.2. Dataset

Size and a revision of Section 4.1.4.5 Data Definition File

June 2015

2.2

Revisions based on comments received to version 2.1. Updates on Table of Contents; Sections

4.1, 4.1.1.2, 4.1.2.3. Updates to Trial Design. Added Exposure as Collected (EC Domain) and

Death Details (DD) subsections. Updates to 4.1.2.2, 4.1.2.3, 4.1.2.4, 4.1.2.5, 4.1.2.6, 4.1.2.8,

4.1.2.9.1, 4.1.2.9.2, 4.1.4 (header and all sub-headers updated to specify which standards

apply), 4.1.4.5, and 4.1.4.6. Added 5.1 subsection; 6.7, 6.7.1, 6.7.1.1. Updates on Section 7,

8.2.2 and Glossary.

October 2015

2.3

Updates to Section 1.3, Exposure as Collected (EC Domain) and Death Details (DD Domain).

Reorganization of Section 4.1.2 and corresponding updates to appropriate sub-sections. Updates

to Sections 4.1.4.5 and 5.1. Added Sections 7.1 and 7.2.

March 2016

3.0

Section 2.2 (Study Data Reviewer’s Guide) - Updated link for SDRG in Footnote 10

Section 3.3.2 (Dataset Size) - Increased Data Set Size

Section 4.1.1.2 (SDTM General Considerations) - Updated to reflect define.xml file and SDRG

reference.

Section 4.1.2.2 (Analysis Data Model - General Considerations) - Updated to reflect define.xml file

and SDRG reference.

Section 4.1.3.2 (Standard for Exchange of Nonclinical Data - General Considerations) - Updated to

reflect define.xml file and SDRG reference.

Section 4.1.4.5 (Data Definition Files for SDTM, SEND, and AdaM) - Updated to reflect define.xml

version 2.0 and data definition specification details

Section 5.1 (Therapeutic Area Standards – General) - Updated to reflect more detailed information

related to Therapeutic Area Standards

Section 5.2 (Supported Therapeutic Area Standards) - Added information related to acceptance testing

on the standard

Section 5.2.1 (Chronic Hepatitis C) - Added Section for this information.

Section 5.2.2 (Dyslipidemia) - Added Section for this information.

Section 6.1.2.1 (Use of the specific controlled term “OTHER”) - Added information related to

controlled terminology and the mapping to “Other”

Section 8.3.1 (Study Data Traceability Overview) - Update to Study Data Traceability flow diagram

reference.

July 2016

3.1

Section 2.1 (Study Data Standardization Plan) Updated to reflect acronym SDSP (Study Data

Standardization Plan) and added footnote 10.

Section 4.1.1.3 (SDTM Domain Specifications) – Updated Trial Design Model (TDM)

Section 4.1.3.3 (SEND Domain Specification) – Added Trial Design (TD)

Section 5.2.3 (Diabetes) - Added Section for this information.

Section 5.2.4 (QT Studies) - Added Section for this information.

Section 5.2.5 (Tuberculosis) - Added Section for this information.

Section 8.2.1.1 (Conformance validation) - Created Section Header and expanded information.

Section 8.2.1.2 (Quality checks) – Created Section Header and updated to reflect study data standard.

Section 8.2.2 (Support on Data Validation Rules) - Expanded information.

Section 3.2 (Portable Document Format) & Glossary – Updated International Council for

Harmonisation (ICH) name

Contains Nonbinding Recommendations

Revision History

Date

Versi

Summary of Revisions

October 2016

3.2

Section 2.2.1 (SDRG for Clinical Data) – Added naming convention

Section 2.2.2 (SDRG for Nonclinical Data) -Added naming convention

Section 2.3 (Analysis Data Reviewer’s Guide) – Provided additional information

Section 3.3.3 (Dataset Column Length) – Expanded Information

Section 4.1.1.2 (SDTM General Considerations) – Expanded Adjudication Data

Section 4.1.2.10 ( Software Programs) – Added more detail related to software programs

Section 4.1.3.2 (General Considerations) – Added VISITDY variable information

Section 4.1.3.3 (SEND Domain Specification) – Added Clinical Observations (CL) Domain.

Expanded Trial Arms and Trial Sets information.

Section 5.1 (General) – Expanded Information

Section 5.2 (Supported Therapeutic Areas) – Expanded Information

Section 7.1 (ECTD File Directory Structure) – Referenced the Guidance to Industry Providing

Regulatory Submissions in Electronic Format: Certain Human Pharmaceutical Product Applications

and Related Submissions Using the Electronic Common Technical Document Specifications and

added footnote

Section 7.2 (ECTD Sample Submission) – Change header to align with detailed information.

Section 8.2.1 (Types of Data Validation Rules) – Expanded Information

Section 8.2.1.1 (Conformance validation) – Expanded Information

Section 8.2.1.2 (FDA Business Rules) – Added new Section

Section 8.2.2 (Support on Data Validation Rules) – updated to reflect conformance rules

Section 8.3.1 (Overview (Study Data Traceability) – added relate counts information

November 2016

3.2.1

Section 8.2.2 (Support on Data Validation Rules) – Footnote 50 Added reference to the Standards

Webpage.

Section 4.1.3.3 (SEND Domain Specification) – Fixed Typo.

Global (Updated naming convention for clinical Study Data Reviewer’s Guide (“cSDRG.pdf”) and

the non-clinical Study Data Reviewer’s Guide (“nSDRG.pdf”) to reflect lower case instead of upper

case. eCTD requires lower case file names.

Contains Nonbinding Recommendations

Table of Contents

1. INTRODUCTION .................................................................................................................................. 1

1.1 BACKGROUND .......................................................................................................................................... 1

1.2 PURPOSE ................................................................................................................................................. 1

1.3 DOCUMENT REVISION AND CONTROL ............................................................................................................ 2

1.4 ORGANIZATION AND SUMMARY OF THE GUIDE ............................................................................................... 2

1.5 RELATIONSHIP TO OTHER DOCUMENTS ......................................................................................................... 3

2. PLANNING AND PROVIDING STANDARDIZED STUDY DATA ................................................................. 4

2.1 STUDY DATA STANDARDIZATION PLAN .......................................................................................................... 4

2.2 STUDY DATA REVIEWER’S GUIDES ................................................................................................................ 4

2.2.1 SDRG for Clinical Data................................................................................................................. 5

2.2.2 SDRG for Nonclinical Data .......................................................................................................... 5

2.3 ANALYSIS DATA REVIEWER’S GUIDE .............................................................................................................. 5

3. EXCHANGE FORMAT – ELECTRONIC SUBMISSIONS ............................................................................. 6

3.1 EXTENSIBLE MARK-UP LANGUAGE ................................................................................................................ 6

3.2 PORTABLE DOCUMENT FORMAT .................................................................................................................. 6

3.3 FILE TRANSPORT FORMAT ........................................................................................................................... 6

3.3.1 SAS Transport Format ................................................................................................................. 6

3.3.2 Dataset Size ................................................................................................................................ 7

3.3.3 Dataset Column Length .............................................................................................................. 7

3.3.4 Variable and Dataset Descriptor Length ..................................................................................... 7

3.3.5 Special Characters: Variables and Datasets ............................................................................... 7

3.3.6 Variable and Dataset Names ...................................................................................................... 7

3.3.7 Variable and Dataset Labels ....................................................................................................... 8

4. STUDY DATA SUBMISSION FORMAT – CLINICAL AND NONCLINICAL ................................................... 8

4.1 CLINICAL DATA INTERCHANGE STANDARDS CONSORTIUM ................................................................................. 8

4.1.1 Study Data Tabulation Model ..................................................................................................... 9

4.1.1.1 Definition ......................................................................................................................................... 9

4.1.1.2 SDTM General Considerations ......................................................................................................... 9

4.1.1.3 SDTM Domain Specifications ......................................................................................................... 10

4.1.2 Analysis Data Model ................................................................................................................. 13

4.1.2.1 Definition ....................................................................................................................................... 13

4.1.2.2 General Considerations .................................................................................................................. 13

4.1.2.3 Dataset Labels ................................................................................................................................ 13

4.1.2.4 Subject Level Analysis Data ............................................................................................................ 13

4.1.2.5 Core Variables ................................................................................................................................ 14

4.1.2.6 Key Efficacy and Safety Variables ................................................................................................... 14

4.1.2.7 Timing Variables ............................................................................................................................. 14

4.1.2.8 Numeric Date Variables ................................................................................................................. 14

4.1.2.9 Imputed Data ................................................................................................................................. 15

4.1.2.10 Software Programs......................................................................................................................... 15

4.1.3 Standard for Exchange of Nonclinical Data .............................................................................. 15

4.1.3.1 Definition ....................................................................................................................................... 15

4.1.3.2 General Considerations .................................................................................................................. 15

4.1.3.3 SEND Domain Specification ............................................................................................................ 16

4.1.4 General Considerations: SDTM, SEND, and/or ADaM .............................................................. 17

4.1.4.1 Variables in SDTM and SEND: Required, Expected, and Permissible ............................................ 17

4.1.4.2 Dates in SDTM and SEND ............................................................................................................... 18

4.1.4.3 Naming Conventions in SDTM and SEND ....................................................................................... 18

4.1.4.4 SDTM and SEND Versions............................................................................................................... 18

Contains Nonbinding Recommendations

4.1.4.5 Data Definition Files for SDTM, SEND, and ADaM.......................................................................... 18

4.1.4.6 Annotated Case Report Form (aCRF) for SDTM ............................................................................. 19

5. THERAPEUTIC AREA STANDARDS ...................................................................................................... 19

5.1 GENERAL ............................................................................................................................................... 19

5.2 SUPPORTED THERAPEUTIC AREAS ............................................................................................................... 20

5.2.1 Chronic Hepatitis C.................................................................................................................... 20

5.2.2 Dyslipidemia ............................................................................................................................. 20

5.2.3 Diabetes .................................................................................................................................... 20

5.2.4 QT Studies ................................................................................................................................. 20

5.2.5 Tuberculosis .............................................................................................................................. 20

6. TERMINOLOGY .................................................................................................................................. 20

6.1 GENERAL ............................................................................................................................................... 20

6.1.1 Controlled Terminologies .......................................................................................................... 21

6.1.2 Use of Controlled Terminologies ............................................................................................... 21

6.1.2.1 Use of the specific controlled term “OTHER” ................................................................................. 22

6.1.3 Maintenance of Controlled Terminologies................................................................................ 22

6.2 CDISC CONTROLLED TERMINOLOGY ........................................................................................................... 23

6.3 ADVERSE EVENTS .................................................................................................................................... 23

6.3.1 MedDRA .................................................................................................................................... 23

6.3.1.1 General Considerations .................................................................................................................. 23

6.4 MEDICATIONS ........................................................................................................................................ 23

6.4.1 FDA Unique Ingredient Identifier .............................................................................................. 23

6.4.1.1 General Considerations .................................................................................................................. 23

6.4.2 WHO Drug Dictionary ............................................................................................................... 24

6.4.2.1 General Considerations .................................................................................................................. 24

6.5 PHARMACOLOGIC CLASS ........................................................................................................................... 24

6.5.1 National Drug File -- Reference Terminology ............................................................................ 24

6.5.1.1 General Considerations .................................................................................................................. 24

6.6 INDICATION ............................................................................................................................................ 25

6.6.1 SNOMED CT .............................................................................................................................. 25

6.6.1.1 General Considerations .................................................................................................................. 25

6.7 LABORATORY TESTS ................................................................................................................................. 25

6.7.1 LOINC ........................................................................................................................................ 25

6.7.1.1 General Considerations .................................................................................................................. 25

7. ELECTRONIC SUBMISSION FORMAT .................................................................................................. 26

7.1 ECTD FILE DIRECTORY STRUCTURE ............................................................................................................. 26

7.2 ECTD SAMPLE SUBMISSION ...................................................................................................................... 29

8. STUDY DATA VALIDATION AND TRACEABILITY .................................................................................. 29

8.1 DEFINITION OF DATA VALIDATION .............................................................................................................. 29

8.2 STUDY DATA VALIDATION RULES ................................................................................................................ 29

8.2.1 Types of Study Data Validation Rules ....................................................................................... 29

8.2.1.1 Conformance Rules ........................................................................................................................ 29

8.2.1.2 FDA Business Rules ........................................................................................................................ 30

8.2.2 Support on Data Validation Rules ............................................................................................. 30

8.3 STUDY DATA TRACEABILITY ....................................................................................................................... 30

8.3.1 Overview ................................................................................................................................... 30

8.3.2 Legacy Study Data Conversion to Standardized Study Data ..................................................... 31

8.3.2.1 Traceability Issues with Legacy Data Conversion ........................................................................... 31

8.3.2.2 Legacy Data Conversion Plan and Report ...................................................................................... 33

Contains Nonbinding Recommendations

iii

APPENDIX: DATA STANDARDS AND INTEROPERABLE DATA EXCHANGE ............................................... 34

GLOSSARY .................................................................................................................................................. 37

Contains Nonbinding Recommendations

STUDY DATA

TECHNICAL CONFORMANCE GUIDE

This technical specifications document represents the Food and Drug Administration's

(FDA's) current thinking on this topic. It does not create or confer any rights for or on

any person and does not operate to bind FDA or the public. You can use an alternative

approach if the approach satisfies the requirements of the applicable statutes and

regulations. If you want to discuss an alternative approach, contact the FDA staff

responsible for implementing this guidance. If you cannot identify the appropriate FDA

staff, send an email to cder-edata@fda.hhs.gov or cber.cdisc@fda.hhs.gov.

1. Introduction

1.1 Background

This Study Data Technical Conformance Guide (Guide) provides specifications,

recommendations, and general considerations on how to submit standardized study data

using FDA-supported

data standards located in the FDA Data Standards Catalog

(Standards Catalog).

The Guide supplements the guidance for industry Providing

Regulatory Submissions in Electronic Format — Standardized Study Data (eStudy Data).

The eStudy Data guidance will implement the electronic submission requirements of

section 745A(a) of the Food Drug & Cosmetic (FD&C) Act with respect to standardized

study data contained in certain investigational new drug applications (INDs), new drug

applications (NDAs); abbreviated new drug applications (ANDAs); and certain biologics

license applications (BLAs) that are submitted to the Center for Drug Evaluation and

Research (CDER) or the Center for Biologics Evaluation and Research (CBER).

1.2 Purpose

This Guide provides technical recommendations to sponsors

for the submission of

animal and human study data and related information in a standardized electronic format

in INDs, NDAs, ANDAs, and BLAs. The Guide is intended to complement and promote

interactions between sponsors and FDA review divisions. However, it is not intended to

replace the need for sponsors to communicate directly with review divisions regarding

implementation approaches or issues relating to data standards. To better understand

why the FDA is now emphasizing the submission of standardized data for all studies,

please refer to the Appendix.

Because of the inherent variability across studies and applications, it is difficult to

identify all data needed by a review division prior to a scientific regulatory review. We

recommend that as early as the pre-IND meeting, sponsors should use the established

For the purposes of this document, “supported” means the receiving Center has established processes and

technology to support receiving, processing, reviewing, and archiving files in the specified file format.

Available at http://www.fda.gov/forindustry/datastandards/studydatastandards/default.htm.

See Providing Regulatory Submissions in Electronic Format — Standardized Study Data (section II.A)

available at http://www.fda.gov/forindustry/datastandards/studydatastandards/default.htm.

For the purposes of this document, the term “sponsor” refers to both “sponsors” and “applicants” who are

submitting study data to the Agency.

Contains Nonbinding Recommendations

regulatory process to discuss with the review division the key data necessary to support a

submission, the data elements that should be included in each dataset, and the

organization of the data within the datasets.

Some data standards may not require the use of all defined data elements to be collected

in any given study. For example, the Study Data Tabulation Model Implementation

Guide (SDTMIG)

classifies variables as required, expected, or permissible. What data

are collected and submitted is a decision that should be made based on scientific reasons,

regulation requirements, and discussions with the review division. However, all study-

specific data necessary to evaluate the safety and efficacy of the medical product should

be submitted in conformance with the standards currently supported by FDA and listed in

the Standards Catalog.

If there is a question regarding a specific submission or a particular data standard

implementation, the sponsor should contact the review division for specific submission

questions or the appropriate contact for data standards issues (cder-edata@fda.hhs.gov or

cber.cdisc@fda.hhs.gov).

This Guide supersedes all previous Study Data Specifications documents (Versions 1.0 -

2.0) and CDER Study Data Common Issues Documents (Versions 1.0 -1.1).

1.3 Document Revision and Control

FDA intends to post updated versions of the Guide to the Study Data Standards

Resources Web page (Standards Web page)

. The plan is to publish updated versions in

March and October of each calendar year. However, this guide will be posted sooner if

important issues arise. The revision history page of the Guide will contain sufficient

information on the changes made by section.

1.4 Organization and Summary of the Guide

This document is organized as follows:

Section 1: Introduction – provides information on regulatory policy and guidance

background, purpose, and document control.

Section 2: Planning and Providing Standardized Study Data – recommends and

provides details on preparing an overall study data standardization plan, a

study data reviewer’s guide and an analysis data reviewer’s guide.

Section 3: Exchange Format - Electronic Submissions – presents the specifications,

considerations, and recommendations for the file formats currently supported

by FDA.

See http://www.cdisc.org.

The Standards Web page can be accessed at

http://www.fda.gov/forindustry/datastandards/studydatastandards/default.htm.

Contains Nonbinding Recommendations

Section 4: Study Data Submission Format: Clinical and Nonclinical – presents

general considerations and specifications for sponsors using, for example, the

following standards for the submission of study data: Study Data Tabulation

Model (SDTM), Analysis Data Model (ADaM), and Standard for Exchange of

Nonclinical Data (SEND).

Section 5: Therapeutic Area Standards – presents supplemental considerations and

specific recommendations when sponsors submit study data using FDA-

supported therapeutic area standards (TA).

Section 6: Terminology – presents general considerations and specific recommendations

when using controlled terminologies/vocabularies for clinical trial data.

Section 7: Electronic Submission Format – provides specifications and

recommendations on submitting study data using the electronic Common

Technical Document (eCTD) format.

Section 8: Study Data Validation and Traceability – provides general

recommendations on conformance to standards, data validation rules, data

traceability expectations, and legacy data conversion.

1.5 Relationship to Other Documents

This Guide integrates and updates information discussed previously in the Study Data

Specifications and the CDER Common Data Standards Issues documents. As noted

above, this Guide supersedes all previous Study Data Specifications documents (Versions

1.0 - 2.0) and CDER Study Data Common Issues Documents (Versions 1.0 -1.1). The

examples of issues and concerns discussed in the Guide are intended as examples only of

common issues, and not an inclusive list of all possible issues.

This Guide is incorporated by reference into the Guidance to Industry Providing

Regulatory Submissions in Electronic Format: Standardized Study Data. In addition,

sponsors should reference the following:

 Study Data Standards Resources Web page (See section 1.3)

 FDA Data Standards Catalog (See section 1.1)

 FDA Portable Document Format Specifications (See section 3.2)

 Guidance to Industry Providing Regulatory Submissions in Electronic Format:

Submissions Under Section 745A(a) of the Federal Food, Drug, and Cosmetic

Act

 Guidance to Industry Providing Regulatory Submissions in Electronic Format:

Certain Human Pharmaceutical Product Applications and Related Submissions

Using the Electronic Common Technical Document Specifications

http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm384686.pdf

www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm333969.pdf

Contains Nonbinding Recommendations

2. Planning and Providing Standardized Study Data

2.1 Study Data Standardization Plan

For clinical and nonclinical studies, sponsors should include a plan (e.g., in the IND)

describing the submission of standardized study data to FDA. The Study Data

Standardization Plan (SDSP) assists FDA in identifying potential data standardization

issues early in the development program. Sponsors may also initiate discussions at the

pre-IND stage. For INDs, the SDSP should be located in the general investigational

plan. The SDSP should include, but is not limited to the following:

1. List of the planned studies

2. Type of studies (e.g., phase I, II or III)

3. Study designs (e.g., parallel, cross-over, open-label extension)

4. Planned data standards, formats, and terminologies and their versions or a

justification of studies that may not conform to the currently supported standards

The FDA’s Study Data Standards Resources Web page provides recommendations for

preparing a SDSP.

The SDSP should be updated in subsequent communications with FDA as the

development program expands and additional studies are planned. Updates to the SDSP

should not be communicated each time a study is started. The cover letter accompanying

a study data submission should describe the extent to which the latest version of the

SDSP was executed.

2.2 Study Data Reviewer’s Guides

The preparation of a Study Data Reviewer’s Guide (SDRG)

is recommended as an

integral part of a standards-compliant study data submission. The SDRG should describe

any special considerations or directions that may facilitate an FDA reviewer's use of the

submitted data and may help the reviewer understand the relationships between the study

report and the data.

http://www.fda.gov/downloads/ForIndustry/DataStandards/StudyDataStandards/UCM447119.pdf

A specific template for a Study Data Standardization Plan is not specified. However, an example of a

Study Data Standardization Plan (template, completion guidelines and examples) can be found at

http://www.phusewiki.org/wiki/index.php?title=Study_Data_Standardization_Plan_%28SDSP%29

A specific template for a Study Data Reviewer’s Guide is not specified. However, an example of a Study

Data Reviewer’s Guide (template, completion guidelines and examples) can be found at

http://www.phusewiki.org/wiki/index.php?title=Study_Data_Reviewer%27s_Guide

For submissions to CBER, sponsors and applicants should continue to provide the Data Interpretation

and Validation Report (DIVR). The DIVR can be incorporated into the Study Data Reviewer’s Guide. The

DIVR can be found at

http://www.fda.gov/BiologicsBloodVaccines/DevelopmentApprovalProcess/ucm209137.htm

Contains Nonbinding Recommendations

There are two study guides: clinical and nonclinical. The SDRG for nonclinical studies

(nSDRG) and SDRG clinical studies (cSDRG) should be placed with the study data in

Module 4 and 5, respectively, in the Electronic Common Technical Document (eCTD).

2.2.1 SDRG for Clinical Data

An SDRG for clinical data should be named csdrg (the prefix ‘c’ designates ‘clinical’)

and the document should be named ‘csdrg’ and provided as a PDF file upon submission

(csdrg.pdf)

The FDA announced in the Federal Register (Docket No. FDA-2015-N-2523) its intent

to review the cSDRG and recommends its use.

2.2.2 SDRG for Nonclinical Data

An SDRG for nonclinical data should be named nsdrg (the prefix ‘n’ designates

‘nonclinical’) and the document should be named ‘nsdrg’ and provided as a PDF file

upon submission (nsdrg.pdf).

2.3 Analysis Data Reviewer’s Guide

The preparation of an Analysis Data Reviewer’s Guide (ADRG)

is recommended as an

important part of a standards-compliant analysis data submission. The ADRG provides

FDA reviewers with context for analysis datasets and terminology, received as part of a

regulatory product submission, additional to what is presented within the data definition

file (i.e., define.xml). The ADRG also provides a summary of ADaM conformance

findings. The ADRG purposefully duplicates limited information found in other

submission documentation (e.g., the protocol, statistical analysis plan [SAP], clinical

study report, define.xml) in order to provide FDA reviewers with a single point of

orientation to the analysis datasets. It should be noted that the submission of an ADRG

does not eliminate the requirement to submit a complete and informative define.xml file

corresponding to the analysis datasets.

 The ADRG for a clinical study should be placed with the analysis data in Module

5 of the Electronic Common Technical Document (eCTD).

 An ADRG for clinical data should be called an ADRG and the document should

be a PDF file ‘ADRG.pdf’ upon submission.

The Study Data Reviewer’s Guides are separate documents from an overall reviewer’s guide which is

placed in Module 1 of the eCTD.

A specific template for an Analysis Data Reviewer’s Guide is not specified. However, an example of an

Analysis Data Reviewer’s Guide (template, completion guidelines and examples) can be found at

http://www.phusewiki.org/wiki/index.php?title=Analysis_Data_Reviewer's_Guide.

Contains Nonbinding Recommendations

3. Exchange Format – Electronic Submissions

3.1 Extensible Mark-up Language

Extensible Mark-up Language (XML), as defined by the World Wide Web Consortium

(W3C), specifies a set of rules for encoding documents in a format that is both human-

readable and machine-readable.

XML facilitates the sharing of structured data across

different information systems. An XML use case is CDISC’s define.xml file. All XML

files should use .xml as the file extension. Although XML files can be compressed, the

define.xml should not be compressed.

3.2 Portable Document Format

Portable Document Format (PDF) is an open file format used to represent documents in a

manner independent of application software, hardware, and operating systems.

A PDF

use case includes, e.g., the annotated CRF (aCRF / blankcrf), and other documents that

align with the International Conference on Harmonization (ICH) M2.

FDA PDF

specifications are located on FDA’s Electronic Common Technical Document (eCTD)

Web site.

The Standards Catalog lists the PDF version(s) that are supported by FDA.

All PDF files should use .pdf as the file extension.

3.3 File Transport Format

3.3.1 SAS Transport Format

The SAS Transport Format (XPORT) Version 5 is the file format for the submission of

all electronic datasets.

The XPORT is an open file format published by SAS Institute

for the exchange of study data. Data can be translated to and from XPORT to other

commonly used formats without the use of programs from SAS Institute or any specific

vendor. There should be one dataset per transport file, and the dataset in the transport file

should be named the same as the transport file (e.g., “ae” and ae.xpt, “suppae” and

suppae.xpt).

XPORT files can be created by the COPY Procedure in SAS Version 5, Version 6 and

higher of the SAS Software. SAS Transport files processed by the SAS CPORT cannot

be reviewed, processed, or archived by FDA. Sponsors can find the record layout for

SAS XPORT transport files through SAS technical document TS-140.

All SAS XPORT

transport files should use .xpt as the file extension. There should be one dataset per

XPORT file and the files should not be compressed.

See http://en.wikipedia.org/wiki/XML.

See http://www.w3.org/XML/.

Adobe Systems Incorporated, PDF Reference, sixth edition, version 1, Nov. 2006, p. 33.

See http://www.ich.org/products/electronic-standards.html.

Available at

http://www.fda.gov/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/ElectronicSubmi

ssions/ucm153574.htm

See http://www.sas.com

Available at http://support.sas.com/techsup/technote/ts140_2.pdf

Contains Nonbinding Recommendations

3.3.2 Dataset Size

Each dataset should be provided in a single transport file. The maximum size of an

individual dataset that FDA can process depends on many factors. Datasets greater than

5 gigabytes (GB) in size should be split into smaller datasets no larger than 5 GB.

Sponsors should submit these smaller datasets, in addition to the larger non-split datasets,

to better support regulatory reviewers. The split datasets should be placed in a separate

sub-directory labeled “split” (See section 7.1). A clear explanation regarding how these

datasets were split needs to be presented within the relevant data reviewer’s guide.

3.3.3 Dataset Column Length

The allotted length for each column containing character (text) data should be set to the

maximum length of the variable used across all datasets in the study except for suppqual

datasets. For suppqual datasets, the allotted length for each column containing character

(text) data should be set to the maximum length of the variable used in the individual

dataset. This will significantly reduce file sizes. For example, if USUBJID has a

maximum length of 18, the USUBJID’s column size should be set to 18, not 200.

3.3.4 Variable and Dataset Descriptor Length

The length of variable names, descriptive labels, and dataset labels should not exceed the

maximum permissible number of characters described below.

Table 1: Maximum Length of Variables and Dataset Elements

Element

Maximum Length in Characters

Variable Name

Variable Descriptive Label

Dataset Label

3.3.5 Special Characters: Variables and Datasets

Variable names, as well as variable and dataset labels should include American Standard

Code for Information Interchange (ASCII) text codes only.

3.3.6 Variable and Dataset Names

Variable and dataset names should not contain punctuation, dashes, spaces, or other non-

alphanumeric symbols. In addition, the variable and dataset names should not contain

special characters, including:

\ / * ,? < > | “ ‘ : % # + ( ) { } [ ]

Contains Nonbinding Recommendations

3.3.7 Variable and Dataset Labels

Variable and dataset labels can include punctuation characters. However, special

characters should not be provided, such as,

1. Unbalanced apostrophe, e.g., Parkinson's.

2. Unbalanced single and double quotation marks.

3. Unbalanced parentheses, braces or brackets, e.g., ‘(‘, ‘{‘and ‘[‘.

4. ‘<’ less-than sign and ‘>’ greater-than sign.

4. Study Data Submission Format – Clinical and Nonclinical

4.1 Clinical Data Interchange Standards Consortium

Clinical Data Interchange Standards Consortium (CDISC) is an open, multidisciplinary,

neutral, nonprofit standards development organization (SDO) that has been working

through consensus-based collaborative teams to develop global data standards for clinical

and nonclinical research.

Data format specifications for the tabulation datasets of clinical and nonclinical

toxicology studies are provided by SDTM and SEND, respectively, while data format

specifications for the analysis datasets of clinical studies are provided by ADaM. It

should be noted that data format specifications for the analysis datasets of nonclinical

toxicology studies have not been developed yet. As noted in section 1.1, the Standards

Catalog provides a listing of the currently supported data standards with links to

reference materials.

Although the SDTM and SEND formats facilitate review of the data, they do not always

provide the data structured in a way that supports all analyses needed for review.

Analysis files are critical for FDA to understand, on a per subject basis, how the specific

analyses contained in the study report have been created. Therefore, sponsors should

supplement the SDTM with ADaM analysis datasets as described below.

There may be instances in which current implementation guides (e.g., SDTMIG,

SENDIG) do not provide specific instruction as to how certain study data should be

represented. In these instances, sponsors should discuss their proposed solution with the

review division and submit supporting documentation that describes these decisions or

solutions in the appropriate SDRG at the time of submission.

See http://www.cdisc.org.

Contains Nonbinding Recommendations

4.1.1 Study Data Tabulation Model

4.1.1.1 Definition

The Study Data Tabulation Model (SDTM) defines a standard structure for human

clinical trials tabulation datasets.

4.1.1.2 SDTM General Considerations

It is recommended that sponsors implement the SDTM standard for representation of

clinical trial tabulation data prior to the conduct of the study. The use of case report

forms that incorporate SDTM standard data elements (e.g., Clinical Data Acquisition

Standards Harmonization (CDASH)) allows for a simplified process for the creation of

SDTM domains.

The SDTMIG should be followed unless otherwise indicated in this Guide or in the

Standards Catalog. The conformance criteria listed in the SDTMIG should not be

interpreted as the sole determinant of the adequacy of submitted data. If there is

uncertainty regarding implementation, the sponsor should discuss application-specific

questions with the review division and general standards implementation questions with

the specific center resources identified elsewhere in this Guide (See section 1.2). Each

submitted SDTM dataset should have its contents described with complete metadata in

the define.xml file (See section 4.1.4.5) and within the cSDRG as appropriate (See

section 2.2). No data should be imputed in SDTM datasets. Data should only be imputed

in ADaM datasets (See section 4.1.2.9).

Except for variables that are defined in the SDTMIG as being coded, no numerically

coded variables should typically be submitted as part of the SDTM datasets. Numeric

values generated from validated scoring instruments or questionnaires do not represent

codes, and therefore have no relevance for this issue. There may be special instances

when codes are preferred, hence sponsors should refer to the review division for

direction, if there are any questions.

Subject Identifier (SUBJID)

The SUBJID is an ID of the entity (i.e., person) that participates in a trial. If the same

subject is screened more than once in a trial, then the subject’s SUBJID should be

different.

Contains Nonbinding Recommendations

Unique Subject Identifier (USUBJID)

Each individual subject should be assigned a single unique identifier across the entire

application. This is in addition to the subject ID (SUBJID) used to identify subjects in

each study and its corresponding study report. An individual subject should have the

exact same unique identifier across all datasets, including between SDTM and ADaM

datasets. Subjects that participate in more than one study should maintain the same

USUBJID across all studies. It is important to follow this convention to enable pooling

of a single subject’s data across studies (e.g., a randomized control trial and an extension

study).

Sponsors should not add leading or trailing spaces to the USUBJID variable in any

dataset. For example, applications have been previously submitted in which the

USUBJID variable for each individual subject appeared to be the same across datasets;

however, in certain datasets, the actual entry had leading zeros added, or zeros added

elsewhere in the entry. This does not allow for machine-readable matching of individual

subject data across all datasets. Improper implementation of the USUBJID variable is a

common error with applications and often requires sponsors to re-submit their data.

Adjudication Data

There are no existing standards or best practices for the representation of adjudication

data as part of a standard data submission. Until standards for adjudication data are

developed, it is advised that sponsors discuss their proposed approach with the review

division and also include details about the presence, implementation approach, and

location of adjudication data in the SDRG.

Whenever adjudication data is provided it should be clearly identified so that the

reviewer can distinguish the results of adjudication from data as originally collected.

4.1.1.3 SDTM Domain Specifications

SUPPQUAL (Supplemental Qualifier)

A SUPPQUAL dataset is a special SDTM dataset that contains non-standard variables

which cannot be represented in the existing SDTM domains. SUPPQUAL should be

used only when key data cannot be represented in SDTM domains. In general, variables

used to support key analyses should not be represented in SUPPQUAL. Discussion with

the review division should occur if the sponsor intends to include important variables

(e.g., that support key analyses) in SUPPQUAL datasets, and reflected in the SDRG.

Contains Nonbinding Recommendations

DM Domain (Demographics)

In the DM domain, each subject should have only one single record per study.

Screen failures, when provided, should be included as a record in DM with the ARM

field left blank. For subjects who are randomized in treatment group but not treated, the

planned arm variables (ARM and ARMCD) should be populated, but actual treatment

arm variables (ACTARM and ACTARMCD) should be left blank.

DS Domain (Disposition)

When there is more than one disposition event, the EPOCH variable should be used to aid

in distinguishing between them. This will allow identification of the EPOCH in which

each event occurred. If a death of any type occurs, it should be the last record and should

include its associated EPOCH. It is expected that EPOCH variable values will be

determined based on the trial design and thus should be defined clearly and documented

in the define.xml.

SE Domain (Subject Elements)

The Subject Elements domain should be included to aid in the association of subject data

(e.g., findings, events, and interventions) with the study element in which they occurred.

AE Domain (Adverse Events (AE))

Currently, there is no variable in the AE domain that indicates if an AE was “treatment-

emergent.” The AE domain should include all adverse events that were recorded in the

subjects’ case report forms, regardless of whether the sponsor determined that particular

events were or were not treatment-emergent.

The entry of a “Y” for the serious adverse event variable, AESER, should have the

assessment indicated, (e.g., as a death, hospitalization, or disability/permanent damage).

Frequently, sponsors omit the assessment information, even when it has been collected on

the CRF. The criteria that led to the determination should be provided. This information

is critical during FDA review to support the characterization of serious AEs.

Custom Domains

The SDTMIG permits the creation of custom domains if the data do not fit into an

existing domain. Prior to creating a custom domain, sponsors should confirm that the

data do not fit into an existing domain. If it is necessary to create custom domains,

sponsors should follow the recommendations in the SDTMIG. In addition, sponsors

should present their implementation approach in the cSDRG.

Although this convention is inconsistent with the SDTMIG, FDA recommends its use so that “Screen

Failure” is not specified as a treatment arm.

Contains Nonbinding Recommendations

LB Domain (Laboratory)

The size of the LB domain dataset submitted by sponsors is often too large to process

(See section 3.3.2). This issue can be addressed by splitting a large LB dataset into

smaller datasets according to LBCAT and LBSCAT, using LBCAT for initial splitting.

If the size is still too large, then use LBSCAT for further splitting. For example, use the

dataset name lb1.xpt for chemistry, lb2.xpt for hematology, and lb3.xpt for urinalysis.

Splitting the dataset in other ways (e.g., by subject or file size) makes the data less

useable. Sponsors should submit these smaller files in addition to the larger non-split

standard LB domain file. Sponsors should submit the split files in a separate sub-

directory/split that is clearly documented in addition to the non-split standard LB domain

file in the SDTM datasets directory (See section 7).

Trial Design Model (TDM)

The SDTMIG TDM should be followed to define the treatment groups and planned visits

and assessments that will be experienced by trial subjects. The TDM defines a standard

structure for representing the planned sequence of events and the treatment plan for the

trial. The TDM includes Trial Arms, Trial Elements, Trial Visit, Trial

Inclusion/Exclusion Criteria, Trial Summary, and Trial Disease Assessment.

All TD datasets should be included, as appropriate for the specific clinical trial, in SDTM

submissions as a way to describe the planned conduct of a clinical trial. Specifically, the

Trial Summary (TS) dataset will be used to determine the time of study start. The

requirement to submit using a particular study data standard is dependent on its support

by FDA as listed in the FDA Data Standards Catalog at the time of study start.

TSPARMCD = SSTDTC will allow the determination of the study start date and should

be included in all SDTM submissions.

As noted in section 1.1, the submission of standardized study data will be required

according to the timetable specified in the eStudy Data guidance. Sponsors submitting

legacy data should provide a TS dataset (ts.xpt) which includes the study start date in the

form of SSTDTC (TSPARMCD = SSTDTC) and TSVAL= “yyyy-mm-dd”.

EC Domain (Exposure as Collected)

The Exposure as Collected domain provides for protocol-specified study treatment

administrations, as-collected. The EC domain may address some challenges in providing

a subject’s exposure to study medication.

DD (Death Details)

The Death Details domain provides for supplemental data that are typically collected

when a death occurs, such as the official cause of death.

Contains Nonbinding Recommendations

4.1.2 Analysis Data Model

4.1.2.1 Definition

Specifications for analysis datasets for human drug product clinical studies are provided

by the Analysis Data Model (ADaM) and its implementation by the ADaMIG. ADaM

datasets should be used to create and to support the results in clinical study reports,

Integrated Summaries of Safety (ISS), and Integrated Summaries of Efficacy (ISE), as

well as other analyses required for a thorough regulatory review. ADaM datasets can

contain imputed data or data derived from SDTM datasets.

4.1.2.2 General Considerations

Generally, ADaM facilitates FDA review. One of the expected benefits of analysis

datasets that conform to ADaM is that they simplify the programming steps necessary for

performing an analysis. As noted above, ADaM datasets should be derived from the data

contained in the SDTM datasets. There are features built into the ADaM standard that

promote traceability from analysis results to ADaM datasets and from ADaM datasets to

SDTM datasets. To ensure traceability, all SDTM variables utilized for variable

derivations in ADaM should be included in the ADaM datasets when practical. Each

submitted ADaM dataset should have its contents described with complete metadata in

the define.xml file (See section 4.1.4.5) and within the ADRG as appropriate (See section

2.3).

4.1.2.3 Dataset Labels

Each dataset should be described by an internal label that is shown in the define.xml file.

The label names of ADaM datasets should be different from those of the SDTM datasets.

For example, the SDTM adverse event dataset (i.e., AE) and the ADaM adverse event

dataset (i.e., ADAE) should not share the exact same dataset label, such as “Adverse

Events.”

4.1.2.4 Subject Level Analysis Data

Subject Level Analysis Data (ADSL) is the subject-level analysis dataset for ADaM. All

submissions containing standard analysis data should contain an ADSL file for each

study. In addition to the variables specified for ADSL in the ADaMIG such as those

listed below in the core variables section (See section 4.1.2.5); the sponsor should include

multiple additional variables representing various important baseline subject

characteristics / covariates presented in the study protocol. Some examples of baseline

characteristics / covariates include, but are not limited to, disease severity scores such as

Acute Physiology and Chronic Health Evaluation (APACHE) scores

, baseline organ

function measurements such as calculated creatinine clearance or Forced Expiratory

Volume in 1 second (FEV1), range categories for continuous variables, and numeric date

variables in non-International Standards Organization (ISO) formats.

Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985). “APACHE II: a severity of disease

classification system.” Critical Care Medicine, 13 (10): 818–829.29.

Contains Nonbinding Recommendations

4.1.2.5 Core Variables

Core variables, which include covariates presented in the study protocol that are

necessary to analyze data, should be included in each ADaM dataset, and are typically

already included in the ADSL dataset (See section 4.1.2.4). The core variables included

in an ADaM dataset should be necessary for the analysis need in that dataset. Examples

of core variables include study/protocol number, center/site number, geographic region,

country, treatment assignment information, sex, age, race, analysis population flags (e.g.,

Intent-to-Treat (ITTFL), Full Analysis Set (FASFL), Safety (SAFFL), and Per-Protocol

(PPROTFL)), and other important baseline demographic variables. Note that all

variables that contain coded data should be accompanied by a variable that provides the

decoded information.

In addition, it is important to note that SDTM datasets do not have core variables (such as

demographic and population variables) repeated across the different domains. The

duplication of core variables across various domains can be fulfilled through their

inclusion in the corresponding analysis datasets. For example, the SDTM AE dataset

does not allow for the inclusion of variables such as treatment arm, sex, age, or race.

These and other variables should be included in the adverse event ADaM dataset (i.e.,

ADAE).

4.1.2.6 Key Efficacy and Safety Variables

Sponsors should submit ADaM datasets to support key efficacy and safety analyses. At

least one dataset should be referenced in the data definition file as containing the primary

efficacy variables. Further, variables pertaining to the primary and secondary endpoints

of a trial, along with their derivations (as applicable), should be provided as well as

documented appropriately (i.e., variable-level metadata or parameter value-level

metadata) in the data definition file.

4.1.2.7 Timing Variables

A variable for relative day of measurement or event, along with timing variables for visit

should be included when an ADaM dataset contains multiple records per subject (i.e.,

repeated measures data).

4.1.2.8 Numeric Date Variables

Numeric date variables are needed for analysis and review purposes. Apply formats to

all numeric date variables using a format that is understandable by SAS XPORT Version

5 files as per Section 3.3.1 above. The software specific (as opposed to trial specific)

date of reference used to calculate numeric dates should be specified within the ADRG.

In the event of partial dates, imputation should be performed only for dates required for

analysis according to the SAP, and appropriate corresponding ADaM imputation flags

should be utilized. When numeric time or date time variables are needed, all

considerations apply as previously discussed for numeric dates.

For traceability purposes, SDTM character dates formatted as ISO 8601 should be

included in the ADaM datasets as well.

Contains Nonbinding Recommendations

4.1.2.9 Imputed Data

When data imputation is utilized in ADaM, sponsors should submit the relevant

supporting documentation (i.e., define.xml and ADRG) explaining the imputation

methods.

4.1.2.10 Software Programs

Sponsors should provide the software programs used to create all ADaM datasets along

with the tables and figures associated with primary and secondary efficacy analyses in

order to help reviewers to better understand how the datasets, tables and figures were

created. The specific software utilized should be specified in the ADRG. The main

purpose of requesting the submission of these programs is to understand the process by

which the variables for the respective analyses were created and to confirm the analysis

algorithms. Sponsors should not submit software programs with executable file

extensions. Sponsors should submit in ASCII text format.

4.1.3 Standard for Exchange of Nonclinical Data

4.1.3.1 Definition

The Standard for Exchange of Nonclinical Data (SEND) provides the organization,

structure, and format of standard nonclinical (animal toxicology studies) tabulation

datasets for regulatory submission. Currently, the SEND Implementation Guide

(SENDIG) supports single-dose general toxicology, repeat-dose general toxicology, and

carcinogenicity studies.

4.1.3.2 General Considerations

The SENDIG provides specific domain models, assumptions, conformance and business

rules, and examples for preparing standard tabulation datasets that are based on the

SDTM. If there is uncertainty regarding SEND implementation, the sponsor should

discuss the issue with the review division.

The ideal time to implement SEND is prior to the conduct of the study as it is very

important that the results presented in the accompanying study report be traceable back to

the original data collected. Each submitted SEND dataset should have its contents

described with complete metadata in the define.xml file (See section 4.1.4.5) and within

the nSDRG as appropriate (See section 2.2).

Sponsors should use the VISITDY variable if findings which were intended to be

analyzed together were collected across multiple study days. This includes postmortem

findings in OM, MA, MI, terminal body weight in BW, and in-life observations in other

Findings domains that are grouped in the Study Report. For example, an ECG might be

collected on Day 20, determined to be uninterpretable, and repeated on Day 21. If those

ECG findings are grouped for analysis in the Study Report, VISITDY should be provided

and set to Day 20 for both ECG collections to provide traceability in the SEND dataset.

Contains Nonbinding Recommendations

4.1.3.3 SEND Domain Specification

SUPPQUAL (Supplemental Qualifier)

A SUPPQUAL dataset is a special SEND dataset that contains non-standard variables

which cannot be represented in the existing SEND domains. Discussion with the review

division should occur if the sponsor intends to include important variables (i.e., that

support key analyses) in SUPPQUAL datasets and this should be reflected in the nSDRG.

Currently, SUPPQUAL should be used to capture some collected information (e.g.,

pathology modifiers) until the SEND is further refined to adequately represent such

information.

Microscopic Findings (MI) Domain

Sponsors should ensure that the transformation of findings from MIORRES to

MISTRESC closely adheres to the instructions in the SENDIG. Modifiers for which there

are variables available (e.g. MISEV, MILAT, etc.) should be placed appropriately.

Severities (e.g., minimal, mild, etc.) should be placed in MISEV, and not duplicated in

MISTRESC or SUPPMI. Non-neoplastic findings in MISTRESC, where controlled

terminology has not yet been established, should be standardized in a way to ensure

traceability between counts in tables, listings, and figures and the terms in MISTRESC.

Clinical Observations (CL) Domain

Only Findings should be provided in CL; ensure that Events and Interventions are not

included. Sponsors should ensure that the standardization of findings in CLSTRESC

closely adheres to the SENDIG. The information in CLTEST and CLSTRESC, along

with CLLOC and CLSEV when appropriate, should contain sufficient information to

ensure traceability between counts in tables, listings, and figures to the unique terms in

CLSTRESC. For example, if “vomitus, food” and “vomitus, clear” are tabulated

separately in the study report, CLSTRESC should be standardized to “vomitus, food” and

“vomitus, clear” rather than “vomitus”. Differences between the representation in CL and

the presentation of Clinical Observations in the Study Report should be mentioned in the

nSDRG.

Custom Domains

The SENDIG allows for the creation of custom domains if the data do not fit into an

existing domain.

Trial Design Model (TDM)

The TDM defines a collection of domains which describe the planned study design.

All TD datasets should be included in SEND submissions as a way to describe the

planned conduct of a nonclinical trial. Specifically, the Trial Summary (TS) dataset will

be used to determine the time of study start. The requirement to submit using a particular

study data standard is dependent on its support by FDA as listed in the FDA Data

Standards Catalog at the time of study start. TSPARMCD = STSTDTC will allow the

determination of the study start date and should be included in all SEND submissions.

Contains Nonbinding Recommendations

Ensure that Trial Arms and Trial Sets represented in TA and TX closely adhere to the

SENDIG in study designs with recovery and/or toxicokinetic animals. Recovery and/or

toxicokinetic animals should typically be presented in separate Trial Sets from the main

arm.

As noted in section 1.1, the submission of standardized study data will be required

according to the timetable specified in the eStudy Data guidance. Sponsors submitting

legacy data should provide a TS dataset (ts.xpt) which includes the study start date in the

form of TSPARMCD = STSTDTC and TSVAL= “yyyy-mm-dd.

Tumor Dataset

Carcinogenicity studies should include an electronic dataset of tumor findings to allow

for a complete review. At this time sponsors should include a tumor.xpt file while

following the specification in the SENDIG for its creation regardless of whether or not

the study is in SEND format (See www.cdisc.org/send).

4.1.4 General Considerations: SDTM, SEND, and/or ADaM

4.1.4.1 Variables in SDTM and SEND: Required, Expected, and Permissible

CDISC data standards categorize SDTM and SEND variables as being Required,

Expected, and Permissible. In some instances, sponsors have interpreted Permissible

variables as being optional and, in other cases, sponsors have excluded Expected

variables. For the purposes of SDTM and SEND submissions, all Required, Expected,

and Permissible variables that were collected, plus any variables that are used to compute

derivations, should be submitted.

SDTM datasets should not contain imputed data. FDA recognizes that SDTM contains

certain operationally derived variables that have standard derivations across all studies

(e.g., --STDY, EPOCH). If the data needed to derive these variables are missing, then

these variables cannot be derived and the values should be null. The following are

examples of some of the Permissible and Expected variables in SDTM and SEND that

should be included, if available:

1. Baseline flags (e.g., last non-missing value prior to first dose) for Laboratory

results, Vital Signs, ECG, Pharmacokinetic Concentrations, and Microbiology

results. Currently, for SDTM, baseline flags should be submitted if the data were

collected or can be derived.

2. EPOCH designators. Please follow CDISC guidance for terminology.

The

variable EPOCH should be included for clinical subject-level observation (e.g.,

adverse events, laboratory, concomitant medications, exposure, and vital signs).

This will allow the reviewer to easily determine during which phase of the trial

See CDISC SDTM Implementation Guide at www.cdisc.org for additional information on variables

referenced throughout this Guide

See http://www.cancer.gov/cancertopics/terminologyresources/page6.

Contains Nonbinding Recommendations

the observation occurred (e.g., screening, on-therapy, follow-up), as well as the

actual intervention the subject experienced during that phase.

3. Whenever --DTC, --STDTC or --ENDTC, which have the role of timing

variables, are included, the matching Study Day variables (--DY, --STDY, or --

ENDY, respectively) should be included. For example, in most Findings

domains, --DTC is Expected, which means that --DY should also be included. In

the Subject Visits domain, SVSTDTC is Required and SVENDTC is Expected;

therefore, both SVSTDY and SVENDY should be included.

4.1.4.2 Dates in SDTM and SEND

Dates in SDTM and SEND domains should conform to the ISO 8601 format. Examples

of how to implement dates are included in the SDTMIG and SENDIG.

4.1.4.3 Naming Conventions in SDTM and SEND

Naming conventions (variable name and label) and variable formats should be followed

as specified in the SDTMIG and SENDIG.

4.1.4.4 SDTM and SEND Versions

When submitting clinical or nonclinical data, sponsors should not mix versions within a

study. As noted above, the Standards Catalog lists the versions that are supported by

FDA.

4.1.4.5 Data Definition Files for SDTM, SEND, and ADaM

The data definition file describes the metadata of the submitted electronic datasets, and is

considered arguably the most important part of the electronic dataset submission for

regulatory review. This data definition specification for submitted datasets defines the

metadata structures that should be used to describe the datasets, variables, possible values

of variables when appropriate, and controlled terminologies and codes. An insufficiently

documented data definition file is a common deficiency that reviewers have noted.

Consequently, the sponsor needs to provide complete detail in this file, especially for the

specifications pertaining to derived variables. In addition, sponsors should also make

certain that the code list and origin for each variable are clearly and easily accessible

from the data definition file. The version of any external dictionary should be clearly

stated both in the data definition file and, where possible, in the updated Trial Summary

(TS) domain (i.e., SDTMIG 3.1.2 or greater; SENDIG 3.0 or greater). The internal

dataset label should also clearly describe the contents of the dataset. For example, the

dataset label for an efficacy dataset might be “Time to Relapse (Efficacy).”

Separate data definition files should be included for each type of electronic dataset

submission, i.e., a separate data definition file for the SDTM datasets of a given clinical

study, a separate data definition file for the SEND datasets of a given nonclinical study,

and a separate data definition file for the ADaM datasets of a given clinical study. The

data definition file should be submitted in XML format, i.e., a properly functioning

define.xml

. In addition to the define.xml, a printable define.pdf should be provided if

See http://www.cdisc.org

See http://www.cdisc.org/define-xml

Contains Nonbinding Recommendations

the define.xml cannot be printed

. To confirm that a define.xml is printable within the

CDER IT environment, it is recommended that the sponsor submit a test version to cder-

edata@fda.hhs.gov prior to application submission. The Standards Catalog lists the

currently supported version(s) of define.xml. It should be noted that define.xml version

2.0 is the preferred version. Sponsors should include a reference to the style sheet as

defined in the specification and place the corresponding style sheet in the same

submission folder as the define.xml file.

4.1.4.6 Annotated Case Report Form (aCRF) for SDTM

An Annotated Case Report Form (aCRF) is a PDF document that maps the clinical data

collection fields used to capture subject data (electronic or paper) to the corresponding

variables or discrete variable values contained within the SDTM datasets. Regardless of

whether the clinical database is legacy or SDTM compliant, an aCRF should be

submitted. The aCRF should be provided as a PDF with the file name “acrf.pdf.”

The

SDTM Metadata Submission Guidelines should be used for additional information on

annotated CRFs.

The aCRF should include treatment assignment forms, when applicable, and should map

each variable on the CRF to the corresponding variables in the datasets (or database). The

aCRF should include the variable names and coding for each CRF item.

When data are recorded on the CRF but are not submitted, the CRF should be annotated

with the text "NOT SUBMITTED.” There should be an explanation in the SDRG stating

why data have not been submitted.

5. Therapeutic Area Standards

5.1 General

CDISC Therapeutic Area (TA) Standards are comprised of existing data elements, but

may introduce new data elements (e.g. domains, variables, terminologies). These data

elements are components of current CDISC implementation guides or will be integrated

into future implementation guides. CDISC publishes a user guide for each therapeutic

area use case which describes the most common data elements for clinical studies

(http://www.cdisc.org/therapeutic). The CDISC Therapeutic Area User Guide (TAUG)

shall not be interpreted as FDA guidance, and may be used as a reference/use case for the

therapeutic area standard.

Detailed FDA PDF specifications are located on FDA’s Electronic Common Technical Document

(eCTD) Web site,

http://www.fda.gov/drugs/developmentapprovalprocess/formssubmissionrequirements/electronicsubmissio

ns/ucm153574.htm

Previously acrf.pdf was called blankcrf.pdf.

See Study Data Tabulation Model Metadata Submission Guidelines (SDTM-MSG)

(http://www.cdisc.org/sdtm).

Contains Nonbinding Recommendations

5.2 Supported Therapeutic Areas

Generally, when a data standard is released for public use by the SDO, it is not supported

by FDA until FDA performs acceptance testing to determine its ability to support new

TA data elements. The sponsor may use the new data elements listed in a Therapeutic

Area User Guide (TAUG) but they are not required until the data elements are included

in a SDTMIG version supported by FDA (the supported SDTMIG is listed in the Data

Standards Catalog). When using new data elements that are not in a SDTMIG currently

supported by FDA, sponsors should describe the rationale for their use in the cSDRG.

The CDISC data elements associated with the following therapeutic areas are currently

supported by FDA (the list will not be published in the Data Standards Catalog):

5.2.1 Chronic Hepatitis C

5.2.2 Dyslipidemia

5.2.3 Diabetes

5.2.4 QT Studies

5.2.5 Tuberculosis

6. Terminology

6.1 General

Common dictionaries should be used across all clinical studies and throughout the

submission for each of the following: adverse events, concomitant medications,

procedures, indications, study drug names, and medical history. FDA recommends that

sponsors use, where appropriate, the terminologies supported and listed in the Standards

Catalog. It is important that coding standards, if they exist, be followed (e.g., ICH

Medical Dictionary for Regulatory Activities (MedDRA) Term Selection: Points-to-

Consider document). Frequently, sponsors submit data that do not conform to

terminology standards, for example, misspelling of MedDRA or WHO Drug terms, lack

of conformance to upper / lower case, or the use of hyphens. All controlled terms

submitted in datasets should conform to the exact case and spelling used by the

terminology maintenance organization (e.g., MedDRA, CDISC controlled terminology).

These conformance issues make it difficult to use or develop automated review and

analysis tools. The use of a dictionary that is sponsor-defined or an extension of a

standard dictionary should be avoided if possible, but, if essential, its use should be

documented in the define.xml file and the SDRG.

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6.1.1 Controlled Terminologies

Controlled terminology standards are an important component of study data

standardization and are a critical component of achieving semantically interoperable data

exchange (See Appendix). Generally, controlled terminology standards specify the key

concepts that are represented as definitions, preferred terms, synonyms, codes, and code

system.

The analysis of study data is greatly facilitated by the use of controlled terms for clinical

or scientific concepts that have standard, predefined meanings and representations.

Standard terminology for adverse events perhaps represents the earliest example of using

standards for study data. For example, myocardial infarction and heart attack are

synonyms, and as such should be mapped to the same term in a standard dictionary. This

level of standardization facilitates an efficient analysis of events that are coded to the

standard term. In electronic study data submissions, sponsors should provide the actual

verbatim terms that were collected (e.g., on the case report form), as well as the coded

term.

Controlled terminology is also useful when consistently applied across studies to

facilitate integrated analyses (that are stratified by study) and cross-study comparative

analyses (e.g., when greater statistical power is needed to detect important safety signals).

Cross-study comparisons and pooled integrated analyses occasionally provide critical

information for regulatory decisions, such as statistical results that support

effectiveness,

as well as important information on exposure-response relationships

and population pharmacokinetics

6.1.2 Use of Controlled Terminologies

FDA recognizes that studies are conducted over many years, during which time versions

of a terminology may change. Generally, FDA expects sponsors to use the most current

version of an FDA-supported terminology available at the time of coding. It is

acceptable to have different studies use different versions of the same dictionary within

the same application. There are some situations where it may be acceptable to use a

single older version of a dictionary across multiple studies, even though that version may

See the guidance for industry Providing Clinical Evidence of Effectiveness for Human Drugs and

Biological Products, available at

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072008.

pdf. We update guidance periodically. To make sure you have the most recent version of guidance, check

the FDA Drugs guidance Web page at

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm.

See the guidance for industry Exposure-Response Relationships — Study Design, Data Analysis, and

Regulatory Applications,

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072109.

pdf.

See the guidance for industry Population Pharmacokinetics, available at

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072137.

pdf.

Contains Nonbinding Recommendations

not be the most current for the later studies. The study data submission should describe

the impact, if any, of the older version on the study results in the SDRG. For example, if

the sponsor anticipates pooling coded data across multiple studies, then it may be

desirable to use a single version across those studies to facilitate pooling. If a sponsor

selects this approach, then the approach and the justification should be documented in the

Standardization Plan, or in an update to the plan.

Regardless of the specific versions used for individual studies, pooled analyses of coded

terms across multiple studies (e.g., for an integrated summary of safety) should be

conducted using a single version of a terminology. This will ensure a consistent and

coherent comparison of clinical and scientific concepts across multiple studies. Sponsors

should specify the terminologies and versions used in the study in the SDRG.

6.1.2.1 Use of the specific controlled term “OTHER”

It is understood that the expansion of controlled terminology may lag behind scientific

advancement, and that sometimes there may not be a relevant term within a controlled

terminology’s value set to describe a clinical trial event, finding, or observation.

However, it is not recommended to map a collected value to “OTHER” when there is a

controlled term available to match the collected value – even when the terminology

allows for Sponsor expansion. Each unique value in a --TERM field mapped to a --

DECODE value of “OTHER” should have a clear rationale outlined in the Study Data

Reviewer’s Guide (clinical or non-clinical).

6.1.3 Maintenance of Controlled Terminologies

The use of supported controlled terminologies is recommended wherever available. If a

sponsor identifies a concept for which no standard term exists, FDA recommends that the

sponsor submit the concept to the appropriate terminology maintenance organization as

early as possible to have a new term added to the standard dictionary. FDA considers

this good terminology management practice. The creation of custom terms (i.e., so-

called extensible code lists) for a submission is discouraged, because this does not

support semantically interoperable study data exchange. Furthermore, the use of custom

or “extensible” code lists should not be interpreted to mean that sponsors may substitute

their own nonstandard terms in place of existing equivalent standardized terms.

Terminology maintenance organizations generally have well-defined change control

processes. Sponsors should allow sufficient time for a proposed term to be reviewed and

included in the terminology, as it is desirable to have the term incorporated into the

standard terminology before the data are submitted. If custom terms cannot be avoided,

the submitter should clearly identify and define them within the submission, reference

them in the SDRG, and use them consistently throughout the application.

If a sponsor identifies an entire information domain

for which FDA has not accepted a

specific standard terminology, they may select a standard terminology to use, if one

By information domain, we mean a logical grouping of clinical or scientific concepts that are amenable

to standardization (e.g., adverse event data, laboratory data, and histopathology data, imaging data).

Contains Nonbinding Recommendations

exists. FDA recommends that sponsors include this selection in the Standardization Plan

(See section 2.1) or in an update to the existing plan, and reference it in the SDRG. If no

controlled terminology exists, the sponsor may define custom terms. The non-FDA

supported terms (whether from a non-supported standard terminology or sponsor-defined

custom terms) should then be used consistently throughout all relevant studies within the

application.

6.2 CDISC Controlled Terminology

Sponsors should use the terminologies and code lists in the CDISC Controlled

Terminology, which can be found at the NCI (National Cancer Institute) Enterprise

Vocabulary Services.

For variables for which no standard terms exists, or if the

available terminology is insufficient, the sponsor should propose its own terms. The

sponsor should provide this information in the define.xml file and in the SDRG.

6.3 Adverse Events

6.3.1 MedDRA

6.3.1.1 General Considerations

MedDRA should be used for coding adverse events. The spelling and capitalization of

MedDRA terms should match the way the terms are presented in the MedDRA dictionary

(e.g., spelling and case). Common errors that have been observed include the incorrect

spelling of a System Organ Class (SOC) and other MedDRA terms.

Generally, the studies included in an application are conducted over many years and may

have used different MedDRA versions. To avoid potential confusion or incorrect results,

the preparation of the adverse event dataset for the ISS should include MedDRA

Preferred Terms from a single version of MedDRA. The reason for an ISS based on a

single version of MedDRA is that reviewers often analyze adverse events across studies,

including the use of Standardized MedDRA Queries.

In addition, sponsors should use

the MedDRA-specified hierarchy of terms. The SDTM variables for the different

hierarchy levels should represent MedDRA-specified primary SOC-coded terms.

6.4 Medications

6.4.1 FDA Unique Ingredient Identifier

6.4.1.1 General Considerations

The FDA Unique Ingredient Identifier (UNII)

should be used to identify active

ingredients (specifically, active moieties) that are administered to investigational subjects

in a study (either clinical or nonclinical). This information should be provided in the

SDTM Trial Summary (TS) domain. UNIIs should be included for all active moieties of

investigational products (TSPARM=TRT or TRTUNII), active comparators

See http://www.cancer.gov/cancertopics/terminologyresources/page6.

See http://www.meddra.org/standardised-meddra-queries.

See http://www.fda.gov/ForIndustry/DataStandards/SubstanceRegistrationSystem-

UniqueIngredientIdentifierUNII/

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(TSPARM=COMPTRT), and any protocol-specified background treatments

(TSPARM=CURTRT).

If a medicinal product has more than one active moiety, then multiple records in TS

should be provided, one for each active moiety. For example, if the investigational

product is Bactrim (a combination of sulfamethoxazole and trimethoprim), then TS will

contain two records for TSPARM=TRT: one for sulfamethoxazole and one for

trimethoprim.

The preferred substance names and UNII codes can be found by searching FDA’s

Substance Registration System, hosted by the National Library of Medicine.

recognize that unapproved substances may not yet have registered UNII codes. We

recommend that sponsors obtain UNII codes for unapproved substances as early in drug

development as possible, so that relevant information, such as study data, can be

unambiguously linked to those substances.

6.4.2 WHO Drug Dictionary

6.4.2.1 General Considerations

World Health Organization (WHO) Drug Dictionary

is a dictionary maintained and

updated by Uppsala Monitoring Centre. WHO Drug Dictionary contains unique product

codes for identifying drug names and evaluating medicinal product information,

including active ingredients and therapeutic uses.

Typically, WHO Drug is used to code concomitant medications. --DECOD should be

populated with the generic name from the WHO dictionary, and --CLAS populated with

the drug class, if the utilized dictionary codes drugs to a single class. When using

WHODRUG, generally, --CLAS would not be filled because a drug may have multiple

classes. However, one Anatomic Therapeutic Code (ATC) level 4 code could be mapped

to --CLAS and the remainder of the ATC codes could be placed in SUPPCM.

6.5 Pharmacologic Class

6.5.1 National Drug File -- Reference Terminology

6.5.1.1 General Considerations

The Veterans Administration’s National Drug File – Reference Terminology (NDF-RT)

should be used to identify the pharmacologic class(es) of all active investigational

substances that are used in a study (either clinical or nonclinical). This information

should be provided in the SDTM Trial Summary (TS) domain. The information should

be provided as one or more records in TS, where TSPARM=PCLAS.

The Substance Registration System can be accessed at http://fdasis.nlm.nih.gov/srs

See http://www.who-umc.org/

See http://mor.nlm.nih.gov/download/rxnav/NdfrtAPIs.html#

Contains Nonbinding Recommendations

Pharmacologic class is a complex concept that is made up of one or more component

concepts: mechanism of action (MOA), physiologic effect (PE), and chemical structure

(CS).

The established pharmacologic class is generally the MOA, PE, or CS term that

is considered the most scientifically valid and clinically meaningful. Sponsors should

include in TS the established pharmacologic class of all active moieties of investigational

products used in a study. FDA maintains a list of established pharmacologic classes of

approved moieties.

If the established pharmacologic class is not available for an active

moiety, then the sponsor should discuss the appropriate MOA, PE, and CS terms with the

review division. For unapproved investigational active moieties where the

pharmacologic class is unknown, the PCLAS record may not be available.

6.6 Indication

6.6.1 SNOMED CT

6.6.1.1 General Considerations

The International Health Terminology Standards Organization’s (IHTSDO) Systematized

Nomenclature of Medicine – Clinical Terms (SNOMED CT)

should be used to identify

the medical condition or problem that the investigational product in a study is intended to

affect (treat, diagnose or prevent, i.e., the indication). This information should be

provided in the SDTM Trial Summary (TS) domain as a record where TSPARM=INDIC

and TSPARM=TDIGRP. SNOMED CT was chosen to harmonize with Indication

information in Structured Product Labeling (SPL)

. A reviewer should be able to take

the indication term from product labeling and readily search for clinical or nonclinical

studies of that indication without having to translate.

6.7 Laboratory Tests

6.7.1 LOINC

6.7.1.1 General Considerations

The Logical Observation Identifiers Names and Codes (LOINC) is a clinical terminology

housed by the Regenstrief Institute.

LOINC codes are universal identifiers for

laboratory and other clinical observations that enable semantically interoperable clinical

data exchange. The laboratory portion of the LOINC database contains the categories of

chemistry, hematology, serology, microbiology (including parasitology and virology),

toxicology, and more.

See the guidance for industry and review staff Labeling for Human Prescription Drug and Biologic

Products —Determining Established Pharmacologic Class for Use in the Highlights of Prescribing

Information, available at

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm186607.

pdf.

Available at

http://www.fda.gov/downloads/ForIndustry/DataStandards/StructuredProductLabeling/UCM346147.zip

http://www.ihtsdo.org/snomed-ct/.

See http://www.fda.gov/ForIndustry/DataStandards/StructuredProductLabeling/ucm163377.htm.

See http://www.regenstrief.org/

Contains Nonbinding Recommendations

The SDTM already supports the exchange of LOINC codes using the LBLOINC

variable.

7. Electronic Submission Format

7.1 eCTD File Directory Structure

Study datasets and their supportive files should be organized into a specific file directory

structure when submitted in the eCTD

format (See Figure 1 and Table 2 below). Note

that this structure is distinct from the eCTD headings and hierarchy folder structure, and

does not affect it. Submission of files within the appropriate folders allows automated

systems to detect and prepare datasets for review, and minimizes the need for manual

processing.

The define.xml and supportive style sheet should reside in the same folder as the datasets

they pertain to (e.g., for SDTM, place in “tabulations\sdtm\”). Do not submit empty file

folders. Do not submit additional subfolders. If you feel that additional folders are

needed, please consult with the appropriate center in advance for guidance.

If you need to split a file that exceeds file size limits (See section 3.3.2), you should

submit the smaller split files in the “split” sub-folder in addition to the larger non-split

file in the original data folder. There is no need for a second define.xml file to be

submitted within the split subfolder.

See http://www.ich.org/products/ctd.html.

Contains Nonbinding Recommendations

For information on how to incorporate datasets into the eCTD, please reference the

“Guidance to Industry Providing Regulatory Submissions in Electronic Format: Certain

Human Pharmaceutical Product Applications and Related Submissions Using the

Electronic Common Technical Document Specifications.” .

The file folder structure

for study datasets is summarized in Figure 1. Table 2 provides the study dataset and file

folder structure and associated description.

Figure 1: Folder Structure for Study Datasets

See “eCTD Technical Conformance Guide” (Electronic Common Technical Document Technical

Conformance Guide (PDF – 160KB)) for further details.

Contains Nonbinding Recommendations

Table 2: Study Dataset and File Folder Structure and Description

Folder Name Folder Level Description/Contents

[module]

Refers to the eCTD module in which study data is being

submitted. Name this folder m4 for nonclinical data and

m5 for clinical data. Do not place files at this level.

datasets

Resides within the module folder as the top-level folder

for study data (nonclinical or clinical) being submitted for

the specified module (m4 or m5). Do not place files at

this level.

[study]

Name this folder with the study identifier or analysis

type performed (e.g., study123, iss, ise). Do not place

files at this level.

analysis

Contains folders for analysis datasets and software

programs; arrange in designated level 6 subfolders. Do

not place files at this level.

adam

Contains subfolders for ADaM datasets and

corresponding software programs. Do not place files at

this level.

datasets

Place ADaM datasets in this subfolder.

split

Place any split ADaM datasets in this subfolder.

programs

Place software programs for ADaM datasets, tables and

figures in this subfolder.

legacy

Contains legacy formatted analysis datasets and

corresponding software programs. Do not place files at

this level.

datasets

Place legacy analysis datasets in this subfolder.

split

Place split legacy analysis datasets in this subfolder.

programs

Place software programs for legacy analysis datasets,

tables and figures in this subfolder.

misc

Place miscellaneous datasets that don’t qualify as

analysis, profile, or tabulation datasets in this subfolder.

This subfolder was formerly named “listings”.

profiles

Place patient profiles in this subfolder.

tabulations

Contains subfolders for tabulation datasets. Do not

place files at this level.

legacy

Place legacy (non-standardized) tabulation datasets in

this folder.

split

Place any split legacy tabulations datasets in this

subfolder.

sdtm

Place SDTM tabulation datasets in this subfolder. Should

only be used in m5 for clinical data.

split

Place any split SDTM files in this subfolder.

send

Place SEND tabulation datasets in this subfolder. Should

only be used in m4 for animal data.

Contains Nonbinding Recommendations

7.2 eCTD Sample Submission

CDER would like to work closely with people who plan to provide a submission using

the eCTD specifications and offer to help smooth the process. The agency also offers a

process for submitting sample standardized datasets for validation. Sample submissions

are tests only and not considered official submissions. They are not reviewed by FDA

reviewers at any time. The Electronic Submissions page provides more information

regarding test submission process.

8. Study Data Validation and Traceability

8.1 Definition of Data Validation

For purposes of this Guide, data validation is a process that attempts to ensure that

submitted data are both compliant and useful. Compliant means the data conform to the

applicable and required data standards. Useful means that the data support the intended

use (i.e., regulatory review and analysis).

8.2 Study Data Validation Rules

Study data validation relies on a set of rules that are used to verify that study data

conform to a minimum set of quality standards. The data validation process can identify

data issues early in the review that may adversely affect the use of the data. FDA

recognizes that it is impossible or impractical to define a priori all the relevant validation

rules for any given submission. Sometimes serious issues in the submitted data are only

evident through manual inspection of the data and may only become evident once the

review is well under way. Often these issues are due to problems in data content (i.e.,

what was or was not submitted, or issues with the collection of original source data),

and not necessarily how the data were standardized.

8.2.1 Types of Study Data Validation Rules

Study data validation is performed using rules that assess whether the data:

1. Conform to a standard listed in the FDA Data Standards Catalog

2. Support regulatory review and analysis.

8.2.1.1 Conformance Rules

Conformance validation rules help ensure that the data conform to the study data

standards listed in the FDA Data Standards Catalog. Standards Development

Organizations (e.g., CDISC) should develop and provide access to rules that assess

conformance to its published standards.

See:

http://www.fda.gov/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/ElectronicSubmi

ssions/ucm174459.htm

Contains Nonbinding Recommendations

8.2.1.2 FDA Business Rules

FDA business rules help to ensure that study data will support meaningful review and

analysis. FDA’s list of business rules will grow and change with agency understanding

and experience. The FDA may publish additional business rules preferring a particular

option when multiple options are offered within a standard.

8.2.2 Support on Data Validation Rules

The Standards Web page

provides links to the currently available business rules.

Sponsors should validate their study data before submission using the conformance rules

published by an SDO and the FDA business rules. Sponsors should either correct any

validation errors or explain in the Reviewer’s Guide (i.e., nSDRG, cSDRG or ADRG)

why certain validation errors could not be corrected. The recommended pre-submission

validation step is intended to minimize the presence of validation errors at the time of

submission.

8.3 Study Data Traceability

8.3.1 Overview

An important component of a regulatory review is an understanding of the provenance of

the data (i.e., traceability of the sponsor’s results back to the CRF data). Traceability

permits an understanding of the relationships between the analysis results (tables, listings

and figures in the study report), analysis datasets, tabulation datasets, and source data.

Traceability enables the reviewer to accomplish the following:

 Understand the construction of analysis datasets

 Determine the observations and algorithm(s) used to derive variables

 Understand how the confidence interval or the p-value was calculated in a

particular analysis

 Relate counts from tables, listings, and figures in a study report to the underlying

data

Based upon reviewer experience, establishing traceability is one of the most problematic

issues associated with legacy study data converted to standardized data. If the reviewer is

unable to trace study data from the data collection of subjects participating in a study to

the analysis of the overall study data, then the regulatory review of a submission may be

compromised. Traceability can be enhanced when studies are prospectively designed to

collect data using a standardized CRF, e.g., CDASH. Traceability can be further

enhanced when a flow diagram is submitted showing how data move from collection

through preparation and submission to the Agency.

See: http://www.fda.gov/downloads/ForIndustry/DataStandards/StudyDataStandards/UCM384744.pdf

Contains Nonbinding Recommendations

8.3.2 Legacy Study Data Conversion to Standardized Study Data

Sponsors should use processes for legacy data conversion that account for traceability.

Generally, a conversion to a standard format will map every data element as originally

collected to a corresponding data element described in a standard. Some study data

conversions will be straightforward and will result in all data converted to a standardized

format. In some instances, it may not be possible to represent a collected data element as

a standardized data element. In these cases, there should be an explanation in the SDRG

as to why certain data elements could not be fully standardized or were otherwise not

included in the standardized data submission. The legacy data (i.e., aCRF, legacy

tabulation data, and legacy analysis data) may be needed in addition to the submission of

converted data.

In cases where the data were collected on a Case Report Form (CRF) or electronic CRF

but were not included in the converted datasets, the omitted data should be apparent on

the annotated CRF and described in the SDRG. The tabular list of studies in the

Standardization Plan should indicate which studies contained previously collected non-

standard data that were subsequently converted to a standard format.

8.3.2.1 Traceability Issues with Legacy Data Conversion

FDA does not recommend a particular approach to legacy study data conversion, but

rather explains the issues that should be addressed so that the converted data are traceable

and adequate to support review.

Table 3 presents some of the issues that can be observed during a review when legacy

study data are converted to SDTM and submitted with legacy analysis datasets.

Table 3: Traceability Issues: Legacy Data Conversion to SDTM Only

1. Limited ability to determine location of collected CRF variables in the converted

SDTM data unless the legacy aCRF is re-annotated.

2. Limited traceable path from SDTM to the legacy analysis data.

3. Limited ability to replicate/confirm legacy analysis datasets (i.e., analysis

variable imputation or derived variables) using SDTM datasets.

4. Limited ability to confirm derivation of intermediate analysis datasets or custom

domains.

5. Difficulty in understanding the source or derivation methods for imputed or

derived variables in integrated/pooled data, supplemental qualifiers, and related

records.

Contains Nonbinding Recommendations

Table 4 presents the issues when legacy study data and legacy analysis data are

independently converted to SDTM and ADaM formats, respectively, rather than ADaM

datasets being created directly from the SDTM datasets (converted from legacy study

data).

Table 4: Traceability Issues: Independent Legacy Data Conversion to SDTM

and ADaM

Issues

1. Limited ability to determine location of collected CRF variables in the converted

SDTM data unless the legacy aCRF is re-annotated.

2. Limited traceable path from SDTM to the legacy analysis data.

3. Limited ability to replicate/confirm legacy analysis datasets (i.e., analysis

variable imputation or derived variables) using SDTM datasets.

4. Limited ability to confirm derivation of intermediate analysis datasets or custom

domains.

5. Limited traceable path from SDTM to the ADaM datasets.

6. Limited ability to replicate ADaM datasets (i.e., analysis variable imputation or

derived variables) using SDTM datasets.

7. Limited traceable path from ADaM to the Tables, Figures and the Clinical Study

Report (CSR).

8. Difficulty in understanding the source or derivation methods for imputed or

derived variables in integrated/pooled data, supplemental qualifiers, and related

records.

Table 5 presents the issues when legacy data are converted to SDTM and ADaM formats

in sequence (i.e., converting legacy study data to SDTM and then creating ADaM from

the SDTM). The key concern is the traceability from ADaM to the Tables, Figures and

CSR.

Table 5: Traceability Issues: Legacy Data Conversion to SDTM and ADaM in

Sequence

1. Limited ability to determine location of collected CRF variables in the converted

SDTM data unless the legacy aCRF is re-annotated.

2. Limited traceable path from SDTM to the legacy analysis data.

3. Limited ability to replicate/confirm legacy analysis datasets (i.e., analysis

variable imputation or derived variables) using SDTM datasets.

4. Limited ability to confirm derivation of intermediate analysis datasets or custom

domains.

5. Limited traceable path from ADaM to the Tables, Figures and the CSR.

6. Difficulty in understanding the source or derivation methods for imputed or

derived variables in integrated/pooled data, supplemental qualifiers, and related

records.

Contains Nonbinding Recommendations

8.3.2.2 Legacy Data Conversion Plan and Report

Sponsors should evaluate the decision involved in converting previously collected non-

standardized data (i.e., legacy study data) to standardized data (i.e., SDTM, SEND, and

ADaM). Sponsors should provide the explanation and rationale for the study data

conversion in the SDRG. To mitigate traceability issues when converting legacy data,

FDA recommends the following procedures:

1. Prepare and Submit a Legacy Data Conversion Plan and Report.

 The plan should describe the legacy data and the process intended for the

conversion.

 The report should present the results of the conversions, issues encountered

and resolved, and outstanding issues.

 The plan and report should be provided in the SDRG.

2. Provide an aCRF, for clinical data, that maps the legacy data elements.

 Sponsors should provide two separate CRF annotations, one based on the

original legacy data, and the other based on the converted data (i.e., SDTM)

when legacy datasets are submitted. The legacy CRF tabulation data should

include all versions and all forms used in the study.

3. Record significant data issues, clarifications, explanations of traceability, and

adjudications in the SDRG. For example, data were not collected or were

collected using different/incompatible terminologies, or were collected but will

not fit into, for example, SDTM format.

4. Legacy data (i.e., legacy aCRF, legacy tabulation data, and legacy analysis data)

may be needed in addition to the converted data.

Contains Nonbinding Recommendations

Appendix: Data Standards and Interoperable Data Exchange

This appendix provides some of the guiding principles for the Agency’s long-term study

data standards management strategies. An important goal of standardizing study data

submissions is to achieve an acceptable degree of semantic interoperability (discussed

below). This appendix describes different types of interoperability and how data

standards can support interoperable data exchange now and in the future.

At the most fundamental level, study data can be considered a collection of data elements

and their relationships. A data element is the smallest (or atomic) piece of information

that is useful for analysis (e.g., a systolic blood pressure measurement, a lab test result, a

response to a question on a questionnaire).

A data value is by itself meaningless without additional information about the data (so

called metadata). Metadata is often described as data about data. Metadata is structured

information that describes, explains, or otherwise makes it easier to retrieve, use, or

manage data.

For example, the number 44 itself is meaningless without an association

with Hematocrit and the unit of measurement (e.g. "%"). Hematocrit in this example is

metadata that further describes the data.

Just as it is important to standardize the representation of data (e.g., M and F for male and

female, respectively), it is equally important to standardize the metadata. The

expressions Hematocrit = 44; Hct = 44, or Hct Lab Test = 44 all convey the same

information to a human, but an information system or analysis program will fail to

recognize that they are equivalent because the metadata is not standardized. It is also

important to standardize the definition of the metadata, so that the meaning of a

Hematocrit value is constant across studies and submissions.

In addition to standardizing the data and metadata, it is important to capture and represent

relationships (also called associations) between data elements in a standard way.

Relationships between data elements are critical to understand or interpret the data.

Consider the following information collected on the same day for one subject in a study:

Systolic Blood Pressure = 90 mmHg

Position = standing

Systolic Blood Pressure = 110 mmHg

Time = 10:23 a.m.

Time = 10:20 a.m.

Metadata is said to “give meaning to data” or to put data “in context.” Although the term is now

frequently used to refer to XML (extensible markup language) tags, there is nothing new about the concept

of metadata. Data about a library book such as author, type of book, and the Library of Congress number,

are metadata and were once maintained on index cards. SAS labels and formats are a rudimentary form of

metadata, although they have not historically been referred to as metadata.

Contains Nonbinding Recommendations

Position = lying

When presented as a series of unrelated data elements, they cannot reliably be

interpreted. Once the relationships are captured, as shown below using arrows, the

interpretation of a drop in systolic blood pressure of 20 mmHg while standing, and

therefore the presence of clinical orthostatic hypotension, is possible. Standardizing

study data therefore involves standardizing the data, metadata, and the representation of

relationships.

Time = 10:20 a.m.  Position = lying  Systolic Blood Pressure = 110 mmHg

Time = 10:23 a.m.  Position = standing  Systolic Blood Pressure = 90 mmHg

With these fundamental concepts of data standardization in mind, data standards can be

considered in the context of interoperable data exchange.

Interoperability

Much has been written about interoperability, with many available definitions and

interpretations within the health care informatics community. In August 2006, the

President signed an Executive Order mandating that the Federal Government use

interoperable data standards for health information exchange.

Although this order was

directed at Federal agencies that administer health care programs (and therefore not the

FDA), it is relevant to this guidance because it defined interoperability for use by Federal

agencies:

“Interoperability” means the ability to communicate and exchange data accurately,

effectively, securely, and consistently with different information technology systems,

software applications, and networks in various settings, and exchange data such that

clinical or operational purpose and meaning of the data are preserved and unaltered.

Achieving interoperable study data exchange between sponsors and applicants and FDA

is not an all-or-nothing proposition. Interoperability represents a continuum, with higher

degrees of data standardization resulting in greater interoperability, which in turn makes

the data more useful and increasingly capable of supporting efficient processes and

analyses by the data recipient. It is therefore useful to understand the degree of

interoperability that is desirable for standardized study data submissions.

In 2007, the Electronic Health Record Interoperability Work Group within Health Level

Seven issued a white paper that characterized the different types of interoperability based

on an analysis of how the term was being defined and used in actual practice.

Three

types of interoperability were identified: technical, semantic, and process

interoperability. A review of these three types provides insight into the desired level of

interoperability for standardized study data submissions.

See http://www.cga.ct.gov/2006/rpt/2006-R-0603.htm.

See Coming to Terms: Scoping Interoperability for Health Care http://www.hln.com/assets/pdf/Coming-

to-Terms-February-2007.pdf.

Contains Nonbinding Recommendations

Technical interoperability describes the lowest level of interoperability whereby two

different systems or organizations exchange data so that the data are useful. The focus of

technical interoperability is on the conveyance of data, not on its meaning. Technical

interoperability supports the exchange of information that can be used by a person but not

necessarily processed further. When applied to study data, a simple exchange of non-

standardized data using an agreed-upon file format for data exchange (e.g., SAS transport

file) is an example of technical interoperability.

Semantic interoperability describes the ability of information shared by systems to be

understood, so that nonnumeric data can be processed by the receiving system. Semantic

interoperability is a multi-level concept with the degree of semantic interoperability

dependent on the level of agreement on data content terminology and other factors. With

greater degrees of semantic interoperability, less human manual processing is required,

thereby decreasing errors and inefficiencies in data analysis. The use of controlled

terminologies and consistently defined metadata support semantic interoperability.

Process interoperability is an emerging concept that has been identified as a

requirement for successful system implementation into actual work settings. Simply put,

it involves the ability of systems to exchange data with sufficient meaning that the

receiving system can automatically provide the right data at the right point in a business

process.

An example of process interoperability in a regulatory setting is the ability to quickly and

automatically identify and provide all the necessary information to produce an expedited

adverse event report in a clinical trial upon the occurrence of a serious and unexpected

adverse event. The timely submission of this information is required by regulation to

support FDA’s mandate to safeguard patient safety during a clinical trial. Process

interoperability becomes important when particular data are necessary to support time-

dependent processes.

Because the vast majority of study data are submitted after the study is complete,

achieving process interoperability for study data submissions in a regulatory setting is

relatively unimportant, at least for the foreseeable future. It is reasonable to conclude that

it is most desirable to achieve semantic interoperability in standardized study data

submissions.

In summary, the goal of standardizing study data is to make the data more useful and to

support semantically interoperable data exchange between sponsors, applicants, and the

FDA such that it is commonly understood by all parties.

Contains Nonbinding Recommendations

Glossary

The following list of acronyms and terms used in this Guide:

aCRF: Annotated Case Report Form

ANDA: Abbreviated New Drug Application

ADaM: Analysis Data Model

ADRG Analysis Data Reviewer’s Guide

ADSL: Subject Level Analysis Data

ASCII: American Standard Code for Information Interchange

CBER: Center for Biologics Evaluation and Research

CDASH: Clinical Data Acquisition Standards Harmonization

CDER: Center for Drug Evaluation and Research

CDISC: Clinical Data Interchange Standards Consortium

CS: Chemical Structure

Domain: A collection of observations with a topic-specific commonality

eCTD: Electronic Common Technical Document

ICH: International Conference on Harmonisation

IND: Investigational New Drug

ISE: Integrated Summary of Efficacy

ISO: International Organization for Standardization

ISO 8601: ISO character representation of dates, date/times, intervals, and durations

of time

ISS: Integrated Summary of Safety

ITT: Intent-To-Treat

MedDRA: Medical Dictionary for Regulatory Activities

MOA: Mechanism of Action

NDA: New Drug Application

NDF-RT: National Drug File – Reference Terminology

PDF: Portable Document Format

PE: Physiologic Effect

SDRG Study Data Reviewer’s Guide

SDTM: Study Data Tabulation Model

SNOMED: Systematized Nomenclature of Medicine

UNII: FDA Unique Ingredient Identifier

WHO: World Health Organization

XML: eXtensible Markup Language

XPORT: SAS Transport Version 5

Contains Nonbinding Recommendations 2016 11 Technical Conformance Guide V3.2.1

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