A Practical Guide To Designing Phase II Trials In Oncology (Statistics Practice) Sarah R. Brown, Walter M. Gregory, Christopher J. Twelves, Julia B

User Manual:

Open the PDF directly: View PDF PDF.
Page Count: 258 [warning: Documents this large are best viewed by clicking the View PDF Link!]

StatiSticS in Practice
Sarah r. Brown
walter M. GreGory
chriS twelveS
Julia Brown
A Practical Guide
to Designing Phase II
Trials in Oncology
A Practical Guide to Designing
Phase II Trials in Oncology
STATISTICS IN PRACTICE
Series Advisors
Human and Biological Sciences
Stephen Senn
CRP-Sant´
e, Luxembourg
Earth and Environmental Sciences
Marian Scott
University of Glasgow, UK
Industry, Commerce and Finance
Wolfgang Jank
University of Maryland, USA
Founding Editor
Vic Barnett
Nottingham Trent University, UK
Statistics in Practice is an important international series of texts which provide
detailed coverage of statistical concepts, methods and worked case studies in specic
elds of investigation and study.
With sound motivation and many worked practical examples, the books show
in down-to-earth terms how to select and use an appropriate range of statistical
techniques in a particular practical eld within each title’s special topic area.
The books provide statistical support for professionals and research workers
across a range of employment elds and research environments. Subject areas cov-
ered include medicine and pharmaceutics; industry, nance and commerce; public
services; the earth and environmental sciences; and so on.
The books also provide support to students studying statistical courses applied to
the above areas. The demand for graduates to be equipped for the work environment
has led to such courses becoming increasingly prevalent at universities and colleges.
It is our aim to present judiciously chosen and well-written workbooks to meet
everyday practical needs. Feedback of views from readers will be most valuable to
monitor the success of this aim.
A complete list of titles in this series appears at the end of the volume.
A Practical Guide to Designing
Phase II Trials in Oncology
Sarah R. Brown
University of Leeds, UK
Walter M. Gregory
University of Leeds, UK
Chris Twelves
St James’s University Hospital, Leeds, UK
Julia Brown
University of Leeds, UK
This edition rst published 2014
© 2014 John Wiley & Sons, Ltd
Registered ofce
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ,
United Kingdom
For details of our global editorial ofces, for customer services and for information about how to apply
for permission to reuse the copyright material in this book please see our website at www.wiley.com.
The right of the author to be identied as the author of this work has been asserted in accordance with the
Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or
transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise,
except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of
the publisher.
Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may
not be available in electronic books.
Designations used by companies to distinguish their products are often claimed as trademarks. All brand
names and product names used in this book are trade names, service marks, trademarks or registered
trademarks of their respective owners. The publisher is not associated with any product or vendor
mentioned in this book.
Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in
preparing this book, they make no representations or warranties with respect to the accuracy or
completeness of the contents of this book and specically disclaim any implied warranties of
merchantability or tness for a particular purpose. It is sold on the understanding that the publisher is not
engaged in rendering professional services and neither the publisher nor the author shall be liable for
damages arising herefrom. If professional advice or other expert assistance is required, the services of a
competent professional should be sought.
Library of Congress Cataloging-in-Publication Data
A practical guide to designing phase II trials in oncology / [edited by] Sarah R. Brown,
Walter M. Gregory, Christopher Twelves, Julia Brown.
p. ; cm.
Includes bibliographical references and index.
ISBN 978-1-118-57090-6 (hardback)
I. Brown, Sarah R., editor of compilation. II. Gregory, Walter M., editor of compilation.
III. Twelves, Chris, editor of compilation. IV. Brown, Julia (Julia M.), editor of compilation.
[DNLM: 1. Clinical Trials, Phase II as Topic. 2. Antineoplastic Agents–therapeutic use.
3. Drug Evaluation–methods. 4. Neoplasms–drug therapy. QV 771.4]
RC271.C5
616.994061–dc23
2013041156
A catalogue record for this book is available from the British Library.
ISBN: 978-1-118-57090-6
Set in 10/12pt Times by Aptara Inc., New Delhi, India
1 2014
To Austin, from Sarah, for your continued support and
encouragement.
And to the many patients and their carers who take
part in clinical trials, often at the most difcult of
times, helping in the development of new and better
treatments for people with cancer now and
in the future.
Contents
Contributors xv
Foreword I xvii
Elizabeth A. Eisenhauer
Foreword II xix
Roger A’Hern
Preface xxi
1 Introduction 1
Sarah Brown, Julia Brown, Walter Gregory and Chris Twelves
1.1 The role of phase II trials in cancer 3
1.2 The importance of appropriate phase II trial design 5
1.3 Current use of phase II designs 6
1.4 Identifying appropriate phase II trial designs 7
1.5 Potential trial designs 9
1.6 Using the guidance to design your trial 10
2 Key points for consideration 12
Sarah Brown, Julia Brown, Marc Buyse, Walter Gregory, Mahesh Parmar and
Chris Twelves
2.1 Stage 1 – Trial questions 14
2.1.1 Therapeutic considerations 14
2.1.2 Primary intention of trial 16
2.1.3 Number of experimental treatment arms 17
2.1.4 Primary outcome of interest 18
2.2 Stage 2 – Design components 18
2.2.1 Outcome measure and distribution 18
2.2.2 Randomisation 21
2.2.3 Design category 26
2.3 Stage 3 – Practicalities 33
2.3.1 Practical considerations 33
2.4 Summary 35
viii CONTENTS
3 Designs for single experimental therapies with a single arm 36
Sarah Brown
3.1 One-stage designs 36
3.1.1 Binary outcome measure 36
3.1.2 Continuous outcome measure 38
3.1.3 Multinomial outcome measure 39
3.1.4 Time-to-event outcome measure 40
3.1.5 Ratio of times to progression 40
3.2 Two-stage designs 41
3.2.1 Binary outcome measure 41
3.2.2 Continuous outcome measure 50
3.2.3 Multinomial outcome measure 50
3.2.4 Time-to-event outcome measure 53
3.2.5 Ratio of times to progression 54
3.3 Multi-stage designs 55
3.3.1 Binary outcome measure 55
3.3.2 Continuous outcome measure 59
3.3.3 Multinomial outcome measure 59
3.3.4 Time-to-event outcome measure 60
3.3.5 Ratio of times to progression 60
3.4 Continuous monitoring designs 60
3.4.1 Binary outcome measure 60
3.4.2 Continuous outcome measure 63
3.4.3 Multinomial outcome measure 63
3.4.4 Time-to-event outcome measure 63
3.4.5 Ratio of times to progression 64
3.5 Decision-theoretic designs 64
3.5.1 Binary outcome measure 64
3.5.2 Continuous outcome measure 65
3.5.3 Multinomial outcome measure 65
3.5.4 Time-to-event outcome measure 65
3.5.5 Ratio of times to progression 65
3.6 Three-outcome designs 65
3.6.1 Binary outcome measure 65
3.6.2 Continuous outcome measure 66
3.6.3 Multinomial outcome measure 66
3.6.4 Time-to-event outcome measure 66
3.6.5 Ratio of times to progression 67
3.7 Phase II/III designs 67
4 Designs for single experimental therapies including randomisation 68
Sarah Brown
4.1 One-stage designs 68
4.1.1 Binary outcome measure 68
4.1.2 Continuous outcome measure 70
CONTENTS ix
4.1.3 Multinomial outcome measure 70
4.1.4 Time-to-event outcome measure 70
4.1.5 Ratio of times to progression 72
4.2 Two-stage designs 72
4.2.1 Binary outcome measure 72
4.2.2 Continuous outcome measure 73
4.2.3 Multinomial outcome measure 74
4.2.4 Time-to-event outcome measure 75
4.2.5 Ratio of times to progression 75
4.3 Multi-stage designs 75
4.3.1 Binary outcome measure 75
4.3.2 Continuous outcome measure 75
4.3.3 Multinomial outcome measure 75
4.3.4 Time-to-event outcome measure 76
4.3.5 Ratio of times to progression 76
4.4 Continuous monitoring designs 76
4.4.1 Binary outcome measure 76
4.4.2 Continuous outcome measure 76
4.4.3 Multinomial outcome measure 76
4.4.4 Time-to-event outcome measure 76
4.4.5 Ratio of times to progression 76
4.5 Three-outcome designs 77
4.5.1 Binary outcome measure 77
4.5.2 Continuous outcome measure 77
4.5.3 Multinomial outcome measure 77
4.5.4 Time-to-event outcome measure 77
4.5.5 Ratio of times to progression 77
4.6 Phase II/III designs 77
4.6.1 Binary outcome measure 77
4.6.2 Continuous outcome measure 79
4.6.3 Multinomial outcome measure 80
4.6.4 Time-to-event outcome measure 81
4.6.5 Ratio of times to progression 81
4.7 Randomised discontinuation designs 82
4.7.1 Binary outcome measure 82
4.7.2 Continuous outcome measure 82
4.7.3 Multinomial outcome measure 82
4.7.4 Time-to-event outcome measure 82
4.7.5 Ratio of times to progression 82
5 Treatment selection designs 83
Sarah Brown
5.1 Including a control arm 84
5.1.1 One-stage designs 84
5.1.2 Two-stage designs 84
x CONTENTS
5.1.3 Multi-stage designs 88
5.1.4 Continuous monitoring designs 89
5.1.5 Decision-theoretic designs 89
5.1.6 Three-outcome designs 89
5.1.7 Phase II/III designs – same primary outcome measure at
phase II and phase III 89
5.1.8 Phase II/III designs – different primary outcome measures
at phase II and phase III 99
5.1.9 Randomised discontinuation designs 102
5.2 Not including a control arm 103
5.2.1 One-stage designs 103
5.2.2 Two-stage designs 106
5.2.3 Multi-stage designs 108
5.2.4 Continuous monitoring designs 109
5.2.5 Decision-theoretic designs 110
5.2.6 Three-outcome designs 110
5.2.7 Phase II/III designs – same primary outcome measure at
phase II and phase III 110
5.2.8 Randomised discontinuation designs 111
6 Designs incorporating toxicity as a primary outcome 112
Sarah Brown
6.1 Including a control arm 112
6.1.1 One-stage designs 112
6.1.2 Two-stage designs 114
6.1.3 Multi-stage designs 115
6.2 Not including a control arm 117
6.2.1 One-stage designs 117
6.2.2 Two-stage designs 118
6.2.3 Multi-stage designs 122
6.2.4 Continuous monitoring designs 125
6.3 Toxicity alone 126
6.3.1 One stage 126
6.3.2 Continuous monitoring 127
6.4 Treatment selection based on activity and toxicity 128
6.4.1 Two-stage designs 128
6.4.2 Multi-stage designs 129
6.4.3 Continuous monitoring designs 129
7 Designs evaluating targeted subgroups 131
Sarah Brown
7.1 One-stage designs 131
7.1.1 Binary outcome measure 131
CONTENTS xi
7.2 Two-stage designs 132
7.2.1 Binary outcome measure 132
7.3 Multi-stage designs 135
7.3.1 Binary outcome measure 135
7.3.2 Time-to-event outcome measure 137
7.4 Continuous monitoring designs 138
7.4.1 Binary outcome measure 138
7.4.2 Time-to-event outcome measure 139
8 ‘Chemo-radio-sensitisation’ in head and neck cancer 141
John Chester and Sarah Brown
Stage 1 – Trial questions 141
Therapeutic considerations 141
Primary intention of trial 142
Number of experimental treatment arms 142
Primary outcome of interest 142
Stage 2 – Design components 142
Outcome measure and distribution 142
Randomisation 143
Design category 143
Possible designs 144
Stage 3 – Practicalities 146
Practical considerations for selecting between designs 146
Proposed trial design 148
Summary 150
9 Combination chemotherapy in second-line treatment of non-small
cell lung cancer 151
Ornella Belvedere and Sarah Brown
Stage 1 – Trial questions 152
Therapeutic considerations 152
Primary intention of trial 152
Number of experimental treatment arms 152
Primary outcome of interest 152
Stage 2 – Design components 153
Outcome measure and distribution 153
Randomisation 153
Design category 153
Possible designs 154
Stage 3 – Practicalities 155
Practical considerations for selecting between designs 155
Proposed trial design 158
Summary 162
xii CONTENTS
10 Selection by biomarker in prostate cancer 163
Rick Kaplan and Sarah Brown
Stage 1 – Trial questions 164
Therapeutic considerations 164
Primary intention of trial 164
Number of experimental treatment arms 164
Primary outcome of interest 164
Stage 2 – Design components 165
Outcome measure and distribution 165
Randomisation 165
Design category 166
Possible designs 167
Stage 3 – Practicalities 168
Practical considerations for selecting between designs 168
Proposed trial design 170
Summary 171
11 Dose selection in advanced multiple myeloma 174
Sarah Brown and Steve Schey
Stage 1 – Trial questions 174
Therapeutic considerations 174
Primary intention of trial 175
Number of experimental arms 175
Primary outcome of interest 175
Stage 2 – Design components 176
Outcome measure and distribution 176
Randomisation 176
Design category 177
Possible designs 177
Stage 3 – Practicalities 178
Practical considerations for selecting between designs 178
Proposed trial design 181
Summary 182
12 Targeted therapy for advanced colorectal cancer 185
Matthew Seymour and Sarah Brown
Stage 1 – Trial questions 185
Therapeutic considerations 185
Primary intention of trial 186
Number of experimental treatment arms 186
Primary outcome of interest 186
Stage 2 – Design components 187
Outcome measure and distribution 187
Randomisation 187
CONTENTS xiii
Design category 188
Possible designs 189
Stage 3 – Practicalities 190
Practical considerations for selecting between designs 190
Proposed trial design 191
Summary 194
13 Phase II oncology trials: Perspective from industry 195
Anthony Rossini, Steven Green and William Mietlowski
13.1 Introduction 195
13.2 Commercial challenges, drivers and considerations 196
13.3 Selecting designs by strategy 197
13.3.1 Basic strategies addressed by phase II studies 198
13.3.2 Potential registration 198
13.3.3 Exploratory activity 203
13.3.4 Regimen selection 204
13.3.5 Phase II to support predicting success in phase III 206
13.3.6 Phase II safety trials 208
13.3.7 Prospective identication of target populations 209
13.4 Discussion 210
References 213
Index 227
Contributors
Sarah Brown Clinical Trials Research Unit, Leeds Institute of Clinical Trials
Research, University of Leeds, UK.
This book was collectively written by Sarah Brown with contributions from:
Ornella Belvedere Department of Oncology, York Hospital, York, UK.
Julia Brown Clinical Trials Research Unit, Leeds Institute of Clinical Trials
Research, University of Leeds, UK.
Marc Buyse International Drug Development Institute, Louvain-la-Neuve, Belgium.
John Chester Institute of Cancer and Genetics, School of Medicine, Cardiff Univer-
sity, and Honorary Consultant, Velindre Cancer Centre, Cardiff, UK.
Steven Green Novartis Pharma AG, Basel, Switzerland.
Walter Gregory Clinical Trials Research Unit, Leeds Institute of Clinical Trials
Research, University of Leeds, UK.
Rick Kaplan Medical Research Council Clinical Trials Unit at University College
London, University College London Hospital, and NIHR Cancer Research Network
Coordinating Centre, UK.
William Mietlowski Novartis Pharma AG, Basel, Switzerland.
Mahesh Parmar Medical Research Council Clinical Trials Unit at University
College London, and NIHR Cancer Research Network Coordinating Centre, UK.
Anthony Rossini Novartis Pharma AG, Basel, Switzerland.
Steve Schey Kings College, London, and Lead Myeloma Clinician, Kings College
Hospital, London, UK.
Matthew Seymour Leeds Institute of Cancer and Pathology, University of Leeds,
and NIHR Cancer Research Network, Leeds and National Cancer Research Institute,
London, UK.
Chris Twelves Leeds Institute of Cancer and Pathology, University of Leeds, and St
James’s University Hospital, Leeds, UK.
Foreword I
The past two decades have seen an unprecedented expansion in the knowledge about
the biological, immunological and molecular phenomena that drive malignancy. This
knowledge has subsequently been translated into a large number of potential anti-
cancer therapeutics and potential predictive or prognostic molecular markers that are
under evaluation in clinical trials.
A key component of the oncology clinical trials development process is the
bridge that must be crossed between the end of phase I evaluation of a drug, at
which time information on its recommended dose, schedule, pharmacokinetic and
pharmacodynamics effects in a small group of individuals is available, and the deni-
tive randomised efcacy trial of that drug in the appropriately dened population of
cancer patients.
This ‘bridge’ is provided by the phase II trial. Historically, phase II oncology stud-
ies sought evidence of sufcient drug efcacy (based on objective tumour response
in a specic cancer type) that large conrmatory phase III trials would be justied.
Those not meeting the efcacy bar would not be pursued in further studies in that
tumour type. In today’s highly competitive environment, the phase II study has come
under scrutiny – some have expressed the concern that too many ‘promising’ drugs
emerging from phase II studies yield negative phase III results, that clinical trial end-
points traditionally deployed in phase II may not be specic or sensitive enough for
today’s molecular-based agents to appropriately direct subsequent drug development
decisions, that efciency is lost if discrete phase II and phase III trials are designed
and that much more should be learned about predictive or selection biomarkers before
and during phase II to optimally guide phase III design.
Numerous papers and opinion pieces on these and other phase II–related topics
have been published in the past decade. Thus this new book by Brown and colleagues:
A Practical Guide to Designing Phase II Trials in Oncology is a welcome addition
to the literature. This comprehensive and well-written guide takes a logical and step-
by-step approach by reviewing and making recommendations on the key variables
that must be considered in phase II oncology trials. Some of these include tailoring
design components to the specic trial question, the approach to studies of single-
and combination-agent trials, when and how randomised and adaptive designs might
be deployed, patient selection and phase II trial endpoints. In addition, the book drills
into issues that may be unique to designs in several specic malignancies such as
xviii FOREWORD I
non-small cell lung cancer, prostate cancer and myeloma. Throughout, examples are
utilised as a means of providing context and guiding the reader.
What is clear is that the phase II oncology trial is not a singular or simple
construct. There is no formula for its design that meets all potential needs. These
trials the ‘shape-shifters’ of the cancer trial spectrum – how they are designed, the
endpoints that are utilised, and the population enrolled depends on the agent and its
associated biology, the type of cancer, the question the trial is intended to address
and how those results are intended to guide future decisions. This comprehensive text
provides much-needed practical information in this important area of clinical cancer
research.
Elizabeth A. Eisenhauer, MD, FRCPC
Head, Department of Oncology
Queen’s University
Kingston, ON, Canada
Foreword II
Twenty years ago, in the early 1990s, the term ‘phase II trial design’ was practically
synonymous with the Simon optimal and MINIMAX two-stage trials (1989) – designs
which have stood the test of time with their pragmatic trade-off between the need to
stop a trial early for inefcacy if response rates were low and the likely overshoot of
interim analysis points in small trials. The Gehan design was also widely used but
many statisticians were wary of designs which focussed on estimation but did not
have distinct success/failure rules which allowed error rates to be tightly specied.
The eld of phase II trial design has expanded rapidly since these early days,
particularly in oncology. Phase I trial design has also been extended over the years to
go beyond mere dose nding and frequently includes an expansion phase at the chosen
dose level which provides initial information on efcacy and pharmacodynamic
predictors of response. Ideally this should enhance the relevance of the subsequent
phase II trials.
This book presents a much-needed guide to contemporary phase II clinical trial
design. Over the years trial endpoints have diversied to include the greater use
of endpoints such as progression free survival that cater for treatments that may
not cause tumour shrinkage and are thought to act by halting cancer cell growth
rather than killing the cell (cytostatic rather than cytotoxic). Recognition of the
inaccuracies inherent in designing trials on the basis of the expected response gleaned
from historical data has also seen more focus on the use of randomisation and the
incorporation of a control group. The increasing emphasis on stratied medicine,
recognising the need to tailor treatments more closely to the biological characteristics
of the individual patient’s disease, has also led to phase II trials designed to address
this need.
The recognition of the division between phase IIa trials designed to investigate
efcacy and phase IIb trials, which focus on determining whether a phase III trial
is worth undertaking, has also been welcome. The latter have increased in size and
complexity in an effort to forestall the possibility of a negative phase III trial. It
has been suggested that as many as two out of every three phase III oncology trials
are negative – a situation which is of real concern, given that drug development is
increasing in expense and comparatively few gain regulatory approval. It is reassuring
to note the number of phase II/III designs that have been developed to closely link
the development of phase II and phase III, but in some situations this is not possible.
xx FOREWORD II
The Simon Optimal Design (Simon 1989) is perhaps the seminal phase II single
arm design, and it is salutary to see how frequently this design is used and has acted
as a springboard for the development of other designs. It is frequently possible to add
judiciously placed interim analyses to trials without increasing the number of patients
or having an adverse effect on the error rates – a manoeuvre which is worth bearing
in mind. For example, the two-stage Simon MINIMAX design, which minimises
the number of patients needed to assess a binary endpoint, is frequently the same
size as the one-stage exact design – on occasion, the MINIMAX design is even
marginally smaller than the single-stage design! The MINIMAX design illustrates
the point that an optional futility interim analysis can be built into a planned one-
stage trial of a binary endpoint without increasing the number of patients or adversely
affecting the error rates. Alternatively, note that a one-stage design can frequently
be converted into a two-stage design by including a futility interim analysis at N/2
(here Nis the xed sample single-stage trial size or could be the number of events
for a time-to-event endpoint). The trial would be stopped on the grounds of futility if
the primary endpoint parameter did not exceed the value under the null hypothesis.
This approach is seen in the design mentioned by Whitehead (2009, Section 4.2.1).
A general boundary rule that I have also used is the p0.001 rule (Peto–Haybittle)
and related to this are common-sense considerations that should not be overlooked.
For example, if ve or more responses in a 41-patient trial are needed to demonstrate
efcacy, as soon as ve responses have been observed the efcacy threshold for the
trial has been passed, and it is clear a phase III trial will be recommended. If the
toxicity prole is acceptable, the fact the efcacy criteria has been met should be
disseminated so that planning for the follow-on phase III trial can commence.
This book will act as a valuable reference source in addition to giving sound
practical guidance. The authors identify a number of areas that have not been explored;
for example, no references were identied for randomised trials with a multinomial
outcome measure (Section 4.1.3). Statisticians who read this book could perhaps ask
themselves which neglected areas they think deserve the highest priority. As regards
phase IIb designs, I would like to see a three-outcome version of the randomised
Simon (2001) design (Section 4.1.4) based on progression-free survival.
Roger A’Hern
Senior Statistician
Clinical Trials and Statistics Unit
Institute of Cancer Research
Sutton, United Kingdom
Preface
Phase II trials are a key element of the drug development process in cancer, rep-
resenting a transition from initial evaluation in relatively small phase I studies, not
only focused on safety but also increasingly incorporating translational studies, to
denitive assessment of efcacy often in large randomised phase III trials. Efcient
design of these early phase trials is crucial to informed decision-making regarding
the future of a drug’s development. There are a number of textbooks available that
discuss statistical issues in early phase clinical trials. These cover pharmacokinetics
and pharmacodynamics studies, through to late phase II trials, and discuss issues
around sample size calculation and methods of analysis. There are few, however,
which focus specically on phase II trials in cancer, and the many elements involved
in their design. Given the large number and variety of phase II trial designs, often
conceptually innovative, and involving multiple components, the purpose of this book
is to provide practical guidance to researchers on appropriate phase II trial design in
cancer.
This book provides an overview to clinical trial researchers of the steps involved
in designing a phase II trial, from the initial discussions regarding the trial idea itself,
through to identication of an appropriate phase II design. It is written as an aid
to facilitate ongoing interaction between clinicians and statisticians throughout the
design process, enabling informed decision-making and providing insight as to how
information provided by clinicians feeds into the statistical design of a trial. The book
acts both as a comprehensive summary resource of traditional and novel phase II trial
designs and as a step-by-step approach to identifying suitable designs.
We wanted to provide a practical and structured approach to identifying appro-
priate statistical designs for trial-specic design criteria, considering both academic
and industry perspectives. A comprehensive library of available phase II trial designs
is included, and practical examples of how to use the book as a resource to design
phase II trials in cancer are given. We have purposely omitted methodological detail
associated with statistical designs for phase II trials, as well as discussion of analysis,
that can be found elsewhere, including in the references for each of the designs listed
in the library of designs.
The book begins with an introduction to phase II trials in cancer and their role
within the drug development process. A structured thought process addressing the
key elements associated with identifying appropriate phase II trial designs is intro-
duced in Chapter 2, including therapeutic considerations, outcome measures and
xxii PREFACE
randomisation. Each of these elements is discussed in detail, describing the different
stages of the thought process around which the guidance is centred. The purpose of
this detailed information is to allow readers to narrow down the number of designs that
are relevant to their trial-specic design criteria. A comprehensive library of phase II
designs is presented in Chapters 3–7, categorised according to design criteria, and a
brief summary of each trial design available is included.
Chapters 8–12 outline a series of practical examples of designing phase II trials in
cancer, providing practical illustration from trial concept to using the library to select
an appropriate trial design. The examples give a avour of how one might apply the
process described within the book, highlighting that there is no ‘one size ts all’
approach to trial design and that there are often many design solutions available to
any one scenario. We hope the book will help researchers to shortlist their options
in order to select an appropriate design to their specic setting, acknowledging other
options that may be considered.
This book has been written predominantly by academic clinical trialists, involving
both clinicians and statisticians. Many of the issues and considerations described
from an academic point of view are, however, also relevant to trials sponsored by
the pharmaceutical industry. The nal chapter of this book describes the design of
phase II trials in cancer from the industry perspective. The commercial perspective
is described in detail, outlining the design processes for phase II trials according to
specic strategic goals. This highlights both the similarities and differences in the
approach to phase II trial design between academia and industry. In the academic
setting there may be more focus on the phase II trial itself and less on the overall
development programme of the drug, compared to industry where the trial is designed
as part of a programme-oriented clinical development plan.
The book is written for both clinicians and statisticians involved in the design
of phase II trials in cancer. Although some elements are written primarily with
statisticians in mind, the discussion around key concepts of phase II trial design,
as well as the practical examples, is accessible to scientists and clinicians involved
in clinical trial design. For those new to early phase trial design, the book provides
an introduction to the concepts behind informed decision-making in phase II trials,
offering a unique and practical learning tool. For those familiar with phase II trial
design, we hope the reader will benet from exposure to new, less familiar trial
designs, providing alternative options to those which they may have previously used.
The book may also be used by postgraduate students enrolled on statistics courses
including a clinical trial or medical module, providing a useful learning tool with
core information on phase II trial design.
We hope that readers will benet from the step-by-step approach described, as
well as from the library of designs presented, enabling informed decision-making
throughout the design process and focused guidance on designs that t researchers’
pre-specied criteria.
Finally, we would like to thank all our colleagues who have contributed to this
book, for their advice and support.
1
Introduction
Sarah Brown, Julia Brown, Walter Gregory
and Chris Twelves
Traditionally, cancer drug development can be dened by four clinical testing phases
(Figure 1.1):
Phase I is the rst clinical test of a new drug after pre-clinical laboratory
studies and is designed to assess the safety, toxicity and pharmacology of
differing doses of a new drug. Typically such studies involve a limited number
of patients and ask the question ‘Is this drug safe?’
Phase II studies are designed to answer the question ‘Is this drug active, and is
it worthy of further large-scale study?’ They predominantly address the short-
term activity of a new drug, as well as assessing further safety and toxicity.
Typically sample sizes for phase II studies range from tens to low hundreds of
patients.
Phase III trials are often large-scale trials of hundreds, even thousands, of
patients and are usually designed to formally evaluate whether a new drug is
more effective in terms of efcacy or toxicity than current treatments. Here the
focus generally is on long-term efcacy, with the aim of identifying practice-
changing new drugs.
Finally, phase IV studies are carried out once a drug is licensed or approved
for a specic indication. Within the pharmaceutical industry setting, phase
IV studies may be designed to collect long-term safety information; in the
academic setting, phase IV trials may investigate the efcacy of a drug outside
of its licensed indication.
A Practical Guide to Designing Phase II Trials in Oncology, First Edition.
Sarah R. Brown, Walter M. Gregory, Chris Twelves and Julia Brown.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
2 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
• Determine dose and
preliminary toxicity
• Sample size–low tens
Phase I
• Establish intermediate
activity
• Gain further toxicity
information
• Sample size–high
tens to hundreds
Phase II
Validate efficacy and
obtain further
toxicity information
• Sample size–
hundreds to
thousands
Phase III
• Post-marketing
surveillance
Phase IV
Figure 1.1 Four clinical phases of drug development.
Presented in this way drug development may appear to be a straight line pathway, but
this is often not the case in practice, with much more time and money invested in large
phase III trials than in other stages of development. Likewise, the boundaries between
the different stages of drug development are increasingly blurred. For example, many
phase I trials treat an expanded cohort of patients at the recommended phase II dose
often at least in part to demonstrate proof of principle or seek evidence of activity.
In recent years a wide range of new ‘targeted’ cancer therapies have emerged with
well-dened mechanisms of action directed at specic molecular pathways relevant
to tumour growth and often anticipated to be used in combination with other standard
treatments. This contrasts with cytotoxic chemotherapy from which the traditional
four phases of cancer drug development emerged. Nevertheless, phase II cancer trials
retain their pivotal position between initial clinical testing and costly, time-consuming
denitive efcacy studies.
The process from pre-clinical development to new drug approval typically takes
up to 10 years and is estimated to cost hundreds of millions of dollars, although
there is some uncertainty over the true costs (Collier 2009). Cytotoxic therapies,
which lack a specic target and mechanism of action, often have a low therapeutic
index, and historically have high rates of failure during drug development due to
lack of efcacy and/or toxicity (Walker and Newell 2009). Although attrition rates
for targeted cancer therapies appear lower than those of cytotoxic drugs, more drugs
progress to expensive late stages of development before being abandoned in cancer
than other therapeutic areas (DiMasi and Grabowski 2007). These worrying statistics
have led to increased attention on clinical trial design, aiming to reduce the attrition
rate and improve the efciency of cancer drug development.
This book focuses on the high-risk transition between phase II and III clinical
trials and provides a practical guide for researchers designing phase II clinical trials
in cancer. There is a clear need for phase II trials that more accurately identify
potentially effective therapies that should move rapidly to phase III trials; perhaps
even more pressing is the need for earlier rejection of ineffective therapies before they
enter phase III testing. On this basis we aim to provide researchers with a detailed
background of the key elements associated with designing phase II trials in patients
with cancer, a thought process for identifying appropriate statistical designs and a
library of available phase II trial designs. The book is not intended to be proscriptive
or didactic, but instead aims to facilitate and encourage an interactive approach by
INTRODUCTION 3
the clinical researcher and the statistician, leading to a more informed approach to
designing phase II oncology trials.
1.1 The role of phase II trials in cancer
Phase II trials in cancer are primarily designed to assess the short-term activity of
new treatments and the potential to move these treatments forward for evaluation of
longer-term efcacy in large phase III studies. In this respect, the term ‘activity’ is
used to describe the ability of an investigational treatment to produce an impact on
a short-term or intermediate clinical outcome measure. We distinguish this from the
term ‘efcacy’ which we use to describe the ability of an investigational treatment
to produce a signicant impact on a longer-term clinical outcome measure such as
overall survival in a denitive phase III trial. Cancer phase II trials are therefore
invariably conducted in the metastatic or neo-adjuvant settings, where measurable
short-term assessments of activity are more easily obtained than in the adjuvant
setting. We focus on phase II trials in cancer, where assessments of ‘activity’ are
usually not immediate and cure not achievable. Nevertheless, many of the statistical
designs available for phase II cancer trials, and concepts discussed, may be applied
to other disease areas.
Phase II trials act as a screening tool to assess the potential efcacy of a new
treatment. That broad description incorporates many different types of phase II trials
including assessing not only traditional evidence of tumour response but also proof
of concept of biological activity, selection between potential doses for further devel-
opment, choosing between potential treatments for subsequent phase III testing and
demonstration that the addition of a new agent to an established treatment appears to
increase the activity of that treatment.
In 1982 Fleming stated that ‘Commonly the central objective of phase II clinical
trials is the assessment of the antitumor “therapeutic efcacy” of a specic treatment
regimen’ (Fleming 1982). More recently the objective of a phase II trial in an idealised
pathway has been described to ‘establish clinical activity and to roughly estimate
clinical response rate in patients’ (Machin and Campbell 2005). Others have taken
this a step further to claim ‘The objective of a phase II trial should not just be to
demonstrate that a new therapy is active, but that it is sufciently active to believe that
it is likely to be successful in pivotal trials’ (Stone et al. 2007a). A common feature
of phase II trials is that their aim is not primarily to provide denitive evidence of
treatment efcacy, as in a phase III study; rather, phase II trials aim to show that a
treatment has sufcient activity to warrant further investigation.
The International Conference on Harmonisation (ICH) Guideline E8: General
Considerations for Clinical Trials prefers to consider classication of study objec-
tives rather than specic trial phases, since multiple phases of trials may incorporate
similar objectives (ICH Expert Working Group 1997). The objectives associated with
phase II trials in the ICH guidance are predominantly to explore the use of the treat-
ment for its targeted indication; estimate or conrm dosage for subsequent studies;
and provide a basis for conrmatory study design, endpoints and methodologies.
4 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Additionally, however, ICH notes that phase II studies, on some occasions, may
incorporate human pharmacology (assessing tolerance; dening or describing
pharmacokinetics/pharmacodynamics; exploring drug metabolism and interactions;
assessing activity) or therapeutic conrmation (demonstrating/conrming efcacy;
establishing a safety prole; providing an adequate basis for assessing benet/risk
relationship for licensing; establishing a dose/response relationship).
These denitions have in common that oncology phase II trials act as an inter-
mediate step between phase I testing on a limited number of patients to establish the
safety of a new treatment and denitive phase III trials aiming to conrm the efcacy
of a new treatment in a large number of patients. The specic aims of a phase II trial
may, however, differ depending on the mechanism of action of the drug in question,
the amount of information currently available on the drug and the setting in which it
is being investigated (e.g. pharmaceutical industry vs. academia). Phase II trials can
be broadly grouped into phase IIa and phase IIb trials. A phase IIa trial may be seen
as seeking proof of concept in the sense of assessing activity of an investigational
drug that has completed phase I development or may investigate multiple doses of a
drug to determine the dose–response relationship. Phase IIa trials may be considered
learning trials and be followed by a decision-making ‘go/no-go’ phase IIb trial to
determine whether or not to proceed to phase III; phase IIb trials may include selec-
tion of a single treatment or dose from many and may include randomisation to a
control arm.
Dose–response can be evaluated throughout the early stages of drug development,
including phase II, but this book does not specically address studies where this is
the primary aim. Many designs are available to assess the dose–response relationship,
perhaps the simplest and most common being the randomised parallel dose–response
design incorporating a control arm and at least two differing dose levels. Cytotoxics
are usually given at the highest feasible dose, but investigating dose–response rela-
tionships may be important with targeted agents that are not necessarily best given
at the maximum possible dose. Such trials serve a number of objectives including
the conrmation of efcacy; the estimation of an appropriate dose; the identication
of optimal strategies for individual dose adjustments; the investigation of the shape
and location of the dose–response curve; and the determination of a maximal dose
beyond which additional benet would be unlikely to occur.
Considerations around choice of starting dose, study design and regulatory issues
in obtaining dose–response information are provided in the ICH Guideline E4: Dose
Response Information to Support Drug Registration (ICH Expert Working Group
1994). Such considerations are, however, outwith the remit of this book, which
focuses on phase II trials designed to assess activity of single-agent or combination
therapies or those designed to select the most active of multiple therapies. We do,
however, discuss phase II selection designs to identify the most active dose from a
number of pre-specied doses rather than specic issues around evaluating dose–
response relationships.
There are often signicant differences between trials conducted within the phar-
maceutical industry and those conducted within academia. Such differences are
predominantly associated with the approach to designing phase II trials, within
INTRODUCTION 5
a portfolio of research, and decision-making around the future development of a
compound or drug. Consequently, the way in which clinical trials are designed,
particularly in the early phase setting, will likely differ between the two environ-
ments. For example, in the academic setting, regardless of the specic aim of the
phase II trial (e.g. proof of concept, go/no-go), decision criteria are pre-specied
to correspond with the primary aim of the trial and form the criteria on which
decision-making and conclusions of the trial are based. Within the pharmaceutical
industry the same pre-dened study aims and objectives apply; however, decision-
making may be complicated by additional factors external to the phase II trial itself,
such as the presence of competitor compounds, patent life or company strategy.
There is inherent pressure within the pharmaceutical industry to achieve timely
regulatory approval and a license indication for a new drug. This does not apply
in the same way within the academic setting where, by the time a drug reaches
phase II testing, it may have been through considerable testing within the phar-
maceutical setting and perhaps be already licensed in alternative disease areas or
in differing combinations or schedules. There are, however, initiatives to facilitate
increased academic/pharmaceutical collaboration in the early stages of drug devel-
opment. Thus, more academic phase II trials may be conducted using novel agents
with only limited clinical data available, so thorough discussion of the aims and
design of these trials becomes even more pertinent. A detailed insight into the indus-
try approach to the design of phase II trials within a developing drug portfolio is
provided in Chapter 13. By contrast, the remainder of this book, including termi-
nology and practical examples of designing phase II trials, draws its focus from the
academic setting.
1.2 The importance of appropriate phase II
trial design
Design of phase II trials is a key aspect of the drug development process. Poor
design may lead to increased probabilities of a false-positive phase II trial resulting
in unnecessary investment in an unsuccessful phase III trial; or a false-negative phase
II resulting in the rejection of a potentially effective treatment. There is a pressing
need for phase II trials to more accurately identify those cancer therapies that will
ultimately be successful in phase III studies and to allow earlier rejection of ineffective
therapies before undertaking costly and time-consuming phase III trials.
As the development of new cancer drugs moves further away from conventional
cytotoxics and more into targeted therapies, the challenges and opportunities in
phase II trial design are ever greater. The choice of phase II design includes not only
statistical considerations, but also decisions regarding the aims of the trial, whether
or not to include randomisation, the choice of endpoints and the size of treatment
effects to be targeted. Each of these elements is critical to ensure the phase II trial
is designed and conducted efciently and that the results of the trial may be used to
make robust, informed decisions regarding future research.
6 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Some researchers have suggested moving directly from phase I to phase III in the
drug development process, on the basis that survival benet in phase III trials may
be observed in the absence of improved response rates therefore rendering phase II
irrelevant (Booth et al. 2008). The potential perils of this approach are demonstrated
by the INTACT1 and INTACT2 trials of getinib in chemotherapy-na¨
ıve advanced
non-small cell lung cancer (NSCLC) patients (Giaccone et al. 2004; Herbst et al.
2004). Phase I trials of getinib in combination with chemotherapy had shown
acceptable tolerability and getinib as monotherapy was active in phase II NSCLC
trials; however, phase II trials of getinib in combination with chemotherapy were
not performed. Subsequently, these two phase III NSCLC trials in over 2000 patients
failed to show improved efcacy with the addition of getinib to cisplatin-based
chemotherapy (Giaccone et al. 2004; Herbst et al. 2004). A conventional, single-arm
NSCLC trial of getinib in combination with chemotherapy may have avoided the
subsequent negative phase III trials. This experience highlights the importance of
designing and conducting appropriately designed and potentially novel phase II trials
prior to embarking on large-scale phase III trials.
1.3 Current use of phase II designs
Several systematic reviews have considered current use of designs in published phase
II trials in cancer (Lee and Feng 2005; Mariani and Marubini 2000; Perrone et al.
2003). Common approaches to trial design included single-arm studies with objective
response as the primary efcacy endpoint, utilising Simon’s two-staged hypothesis
testing methods (Simon 1989), and randomised trials based on single-arm designs
embedded in a randomised setting (Lee and Feng 2005). All highlighted a distinct
lack of detail regarding an identiable statistical design, and design characteristics,
as a marked weakness of many published phase II studies, raising the possibility
that low quality may bias study ndings. Also striking is the consistent use of a
limited number of the same phase II study designs, emphasising the need for better
understanding of alternative statistical designs. A key recommendation from these
reviews is better communication between statisticians and clinical trialists to increase
the use of newer statistical designs. Likewise, the need for ‘the development of
practical designs with good statistical properties and easily accessible computing
tools with friendly user interface’ (Lee and Feng 2005) is recognised as essential so
statisticians can implement these new designs.
In 2009 the Journal of Clinical Oncology (JCO) published an editorial making
recommendations for the types of phase II trials that they would consider for publi-
cation (Cannistra 2009). The differing aims of phase II trials according to the nature
of the treatment under investigation were identied, with discussion as to the likely
priority given to each trial design. The specic categories and outcomes of phase II
trials were
single-arm phase II studies that represent the rst evidence of activity of a new
drug class;
INTRODUCTION 7
phase II studies of novel agents that not only conrm a class effect, but also
provide evidence of extraordinary and unanticipated activity compared to prior
agents in the same class;
phase II studies of an agent or regimen with prior promise (based on previous
reports of clinical activity), but that are convincingly negative when studied
more rigorously;
phase II studies of a single-agent or combination that convincingly demonstrate
a new, serious and unanticipated toxicity signal, despite being a rational and
potentially active regimen;
phase II studies with biomarker correlates that validate mechanism of action,
provide convincing insight into novel predictive markers or permit enrichment
of patients most likely to benet from a novel agent;
randomised phase II studies such as randomised selection, randomised com-
parison and randomised discontinuation designs.
The consistent use of single-arm, two-stage, response-driven designs as depicted
in the systematic reviews described previously would not optimally cover the majority
of these trial scenarios. The categories listed above were intended to provide authors
with guidance as to the types of phase II trials most relevant to informing the design of
subsequent phase III trials. Such recommendations highlight the need for awareness of
the many components contributing to the design of phase II trials and the importance
of making informed decisions to achieve the objectives of a trial and ensure the results
are robust and interpretable.
1.4 Identifying appropriate phase II trial designs
This book aims to provide guidance to both the clinical researcher and statistician
on each of the key elements of phase II trial design, enabling an understanding of
how they inform the overall design process. Recommendations published by the
Clinical Trial Design Task Force of the National Cancer Institute Investigational
Drug Steering Committee (Seymour et al. 2010) and by the Methodology for the
Development of Innovative Cancer Therapies (MDICT) Task Force (Booth et al.
2008) provide guidance on current best practice for individual aspects of early clinical
trial design. General discussion of choice of endpoints and use of randomisation
is given for the differing settings of monotherapy and combination therapy trials
(Seymour et al. 2010), as well as in the specic context of targeted therapies (Booth
et al. 2008), and discussion on reporting of phase II trials is also provided. Neither set
of recommendations, however, provides detailed guidance on the statistical design
categories available for phase II trials. Here we aim to guide researchers in a step-
by-step manner through the thought process associated with each element of phase
II design, from initial trial concept to the identication of an appropriate statistical
design. With detailed discussion on each of the elements we aim to provide researchers
8 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
with a thorough understanding of the overall process and each of the stages involved,
therefore providing a more informed approach.
Central to this approach is an overall thought process, presented in detail in Chap-
ter 2 and outlined briey below. The approach consists of three stages, highlighting
eight key elements associated with identifying an appropriate phase II trial design:
Stage 1 – Trial questions:
Therapeutic considerations
Primary intention of trial
Number of experimental treatment arms
Primary outcome of interest
Stage 2 – Design components:
Outcome measure and distribution
Randomisation
Design category
Stage 3 – Practicalities:
Practical considerations
Each of these elements is discussed in detail in Chapter 2, and practical examples
of using this approach to design phase II cancer trials are provided in Chapters 8–12.
These elements were identied as being essential to the design of phase II trials in
cancer through a comprehensive literature review of available statistical methodology
for phase II trials (Brown et al. 2011). The thought process itself is iterative, such that
information obtained during discussion of each element may feed into and inform
later elements of the design. The starting point of any trial design should, however, be a
discussion between the clinical researcher and the statistician that primarily concerns
clinical factors relating to the specic treatment(s) under investigation (Stage 1).
Continued interaction between the clinician and the statistician is essential throughout
the design process.
Using the detail provided in Chapter 2, each of the elements is addressed in
turn and iteratively. Decisions made throughout the process enable the statistician to
narrow down the specic statistical designs appropriate to the pre-specied criteria.
These statistical designs are provided in Chapters 3–7, a library resource of statistical
designs, as introduced here. Each design is categorised to enable efcient navigation
and identication of appropriate designs. Designs are laid out taking into account
The use of randomisation including
Single-arm designs, arranged by design category and outcome measure –
Chapter 3
INTRODUCTION 9
Randomised designs, arranged by design category and outcome measure –
Chapter 4
Treatment selection designs, arranged by inclusion of a control arm, design
category and outcome measure – Chapter 5
The focus on both activity and toxicity, or toxicity alone, as the primary outcome
of interest – Chapter 6
The evaluation of treatment activity in targeted subgroups – Chapter 7
Within each of Chapters 3–5, where there is no identied literature for spe-
cic design category and outcome measure combinations, this is highlighted within
the relevant subsection. For example, there were no references identied discussing
single-arm trial designs specically focused on continuous outcome measures, there-
fore this subsection is included to highlight this to the reader. For Chapters 6 and 7,
only those specic design category and outcome measure combinations for which
references have been identied are listed, since generally there are fewer designs
focused on activity and toxicity and targeted subgroups.
In the majority of cases there will be more than one statistical design that suits the
pre-specied trial parameters determined via the thought process. In such cases, the
nal stage in the thought process, that of practical considerations, may allow a choice
to be made between the alternatives. On the other hand, that choice may be based
on previous experience or assessment of various trial scenarios by mathematical
modelling or simulation. Further detail on choosing between multiple designs is
provided in Chapter 2.
1.5 Potential trial designs
The statistical designs summarised in Chapters 3–7 were identied from a compre-
hensive literature review of phase II statistical design methodology conducted in
January 2008 and updated in January 2010 (Brown et al. 2011). Individual designs
were specically assessed to determine their ease of implementation. Designs were
dened as not easy to implement if
the data required to enable implementation were not likely to be available;
there was no sample size justication rendering the design difcult or impos-
sible to interpret;
criteria were not specied for the study being positive or negative as this makes
the trial of little if any use in taking a new treatment forward;
each patient needed to be assessed prior to the next patient being recruited, as
this will usually be prohibitively restrictive in a phase II cancer trial; and
the necessary statistical softwares were not detailed as being available and/or
insufcient detail was provided to enable implementation.
10 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
While this assessment of ease of implementation is inherently subjective, these
criteria reect the practicalities of design implementation.
Applying the above criteria, those designs classed as being easy to implement
are included in Chapters 3–7. This amounts to over 100 statistical designs, ranging
from Gehan’s original two-stage design published in 1961 (Gehan 1961) to com-
plex multi-arm, multi-stage designs of more recent years. Mariani and Marubini
highlighted researchers’ preferences for single-arm, two-stage designs (Mariani and
Marubini 2000); there are, however, a wealth of alternative designs available, ranging
from adaptations of Simon’s original two-stage design to incorporate adjustments for
over-/under-recruitment, to randomised trials with formal hypothesis testing between
experimental and control arms. The intention of this book is to present researchers
with the designs available to them for their specic trial, rather than to recommend
one design over another. In doing so we incorporate the well-established designs of
Gehan (1961), Fleming (1982) and Simon (1989), as well as bringing lesser known
designs to the attention of researchers, allowing the user to make informed choices
regarding trial design. A brief overview of each design identied is presented; how-
ever, the technical detail of each design is omitted and may be further evaluated by
considering the complete references, as appropriate.
With the continued development of targeted therapies in cancer, and a drive
towards personalised medicine, the role of biomarkers within phase II trials is an
important area for discussion. Where known biomarkers are available to identify
selected patient populations most likely to benet from an intervention, phase II trials
may be designed as enrichment trials, whereby only biomarker-positive patients are
included. In these cases, any of the statistical designs listed within Chapters 3–6
may be appropriate, focusing solely on the target population. Alternatively, when
selected populations are perhaps less well validated, biomarker-stratied designs
may be considered. Here both biomarker-positive and biomarker-negative subgroups
are explored within a trial, ensuring adequate numbers of patients within each cohort
to potentially detect differing treatment effect sizes. Such designs are listed within
Chapter 7. A more detailed discussion of the incorporation of biomarkers within phase
II trials in cancer is provided in Chapter 2. There have, however, been a number of
recently published articles in this area that may not be included in the library of
available statistical designs since they post-date the updated systematic review on
which the library is based. Where the incorporation of biomarkers is of particular
relevance to a trial design, the researcher may use the thought process described
within this book and should consider not only any appropriate designs identied in
Chapters 3–7, but also additional, more recent, designs specically intended for trials
incorporating biomarkers.
1.6 Using the guidance to design your trial
We present a thought process for the design of phase II trials in cancer, introduced
briey in Section 1.4, addressing the key elements associated with identifying an
appropriate trial design; each of these elements is discussed in detail in Chapter 2.
INTRODUCTION 11
The information in Chapter 2 will allow researchers to narrow down the number of
appropriate designs for their trial and then navigate to the relevant designs in Chapters
3–7, where a brief summary of each trial design is provided. The statistical theory
underpinning the designs detailed is published elsewhere (Mariani and Marubini
1996; Machin et al. 2008; Machin and Campbell 2005), as well as in the individual
papers referenced.
This process is illustrated in Chapters 8–12 by a series of practical, real-life exam-
ples of designing phase II trials in cancer following the thought process and library
of statistical designs. The examples are intended merely as pragmatic illustrations of
how one might apply the process described within the book; they should not be taken
as sole solutions to trial design under the particular settings presented. It is acknowl-
edged that there may be a number of appropriate designs available, and exploration
of various possibilities is encouraged. Examples are presented in the setting of head
and neck cancer, lung cancer, prostate cancer, myeloma and colorectal cancer. Each
example gives differing trial design scenarios highlighting various common issues
encountered when designing phase II trials in cancer. These examples demonstrate
the types of discussions expected between statisticians and clinicians in order to
extract the necessary information to design a phase II trial. They also provide practi-
cal advice regarding how choice of design may be made when several designs t the
trial-specic requirements.
2
Key points for consideration
Sarah Brown, Julia Brown, Marc Buyse, Walter
Gregory, Mahesh Parmar and Chris Twelves
Designing a phase II trial requires ongoing discussion between the clinician, statis-
tician and other members of the trial team, so the design can evolve on the basis of
information specic to each trial. Central to the approach of identifying an optimal
phase II trial design is the thought process introduced in Chapter 1, and presented
diagrammatically in Figure 2.1. The process provides an overview of the key stages
and elements for consideration during the phase II trial design process. Each of these
elements should be worked through in turn in an iterative manner as information
derived at earlier stages feeds in to design choices and decisions in the latter stages
and consideration of alternative designs.
The thought process is made up of three stages:
Stage 1 – Trial questions. This stage elicits information predominantly relating
to the trial itself in relation to the treatment under investigation, the primary
intention of the trial, number of arms and primary outcome of interest.
Stage 2 – Design components. The information from the rst stage feeds
into the discussions relating to design components considering the outcome
measure, randomisation (or not) and category of design, enabling attention to
be focused on the specic statistical designs relevant to the trial.
Stage 3 – Practicalities. Finally, practical considerations may inform which,
from a number of candidate trial designs, is the one best suited to a particular
situation.
A Practical Guide to Designing Phase II Trials in Oncology, First Edition.
Sarah R. Brown, Walter M. Gregory, Chris Twelves and Julia Brown.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
Targeted
subgroups
Stage1 – trial
questions
One-stage
Two-stage
Multi-stage
Continuous
monitoring
Decision-theoretic
Three-outcome
PhaseII/III
Randomised
discontinuation
Designcategory
Outcomemeasure
anddistribution
Binary(e.g.
response/no
response)
Multinomial
(e.g.CRvs.
PRvs.
SD/PD)
Continuous
(e.g.
biomarker)
Time-to-
event
Ratioof
timesto
progression
Primary
outcomeof
interest
Activity
Activityand
toxicityor
Toxicity
Primary
intentionof
trial
Proofof
concept
Go/no-go
decisionfor
phaseIII
Randomisation
Single arm(no
randomisation)
Randomisation
toexperimental
arms(selection)
Randomisation
incl.control,
withnoformal
comparison
(referencearm
only)
Randomisation
incl.control,
withformal
comparison
Practical
considerations
Availability/
robustnessof
priordata
Early
termination
forlackof
activity
Programming
requirements
Early
termination
forevidence
ofactivity
Numberof
experimental
treatmentarms
One
Morethan
one
Therapeutic
considerations
Mechanism
ofaction
Singleor
combination
therapy
Biomarker
dependent
(enrichment
orendpoint)
Aimof
treatment
Stage2 – design
components
Stage3 –
practicalities
Operating
characteristics
Figure 2.1 Thought process for identifying phase II trial designs.
14 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
This chapter works through each of the stages and components of Figure 2.1.
2.1 Stage 1 – Trial questions
2.1.1 Therapeutic considerations
The choice of trial design depends not only on statistical considerations, but more
importantly on the clinical factors relating to the treatment(s) and/or disease under
investigation. Discussion of these therapeutic considerations is essential to inform
decisions to be made later in the thought process. At the rst meeting between the
clinician and statistician, discussion of the following points will provide an overview
of the setting of the trial and the specic therapeutic issues to be incorporated into
the trial design.
2.1.1.1 Mechanism of action
An important question to ask when beginning the trial design process is ‘how does
this treatment work?’ The term ‘cytotoxic’ may be used to describe chemothera-
peutic agents, where tumour shrinkage or response is widely accepted as reecting
anti-cancer activity. Many new cancer therapies are, however, targeted at specic
molecular pathways relevant to tumour growth, apoptosis (programmed cell death)
or angiogenesis (new blood vessel formation). Such ‘targeted therapies’, including
tyrosine kinase inhibitors, monoclonal antibody therapies and immunotherapeutic
agents, may be ‘cytostatic’. Here, a change in tumour volume may not be the expected
outcome: in such cases, tumour stabilisation or delay in tumour progression may be
a more anticipated outcome.
The mechanism of action of the agent under investigation will inform many
subsequent decisions, including the choice of outcome measure and whether or not
the trial should be randomised.
2.1.1.2 Aim of treatment
The aim of the treatment under investigation should be considered both in the context
of its mechanism of action and the specic population of patients in which the
treatment is being considered.
It is important to consider the ultimate aim of treatment, which would inform the
outcome measures in future phase III studies, and how this relates to shorter term
aims that can be incorporated into phase II trials. For example, in a population of
patients with a relatively long median progression-free survival (PFS) and overall
survival (OS), the aim of a phase III trial may be to prolong further PFS and/or
OS. These would, however, be unrealistic short-term outcomes for a phase II trial;
tumour response, which may reect PFS or OS, can be an appropriate shrinkage aim
in a phase II trial. By contrast, where the prognosis is less good PFS may provide a
realistic short-term outcome in phase II.
KEY POINTS FOR CONSIDERATION 15
It is essential to consider how the longer term and shorter term aims of treatment
are related, to ensure an appropriate intermediate outcome measure is chosen in phase
II that provides a robust assessment of potential efcacy in subsequent phase III trials.
2.1.1.3 Single or combination therapy
It is important to ascertain whether the treatment under investigation will be given
as a single agent or in combination with another novel or established intervention.
This distinction can inform the decision as to whether or not randomisation should
be incorporated. Where an investigational agent, be it a conventional cytotoxic or a
targeted agent, is used in combination with another active treatment it can be very
difcult to distinguish the effect of the investigational agent from that of the standard
partner therapy; this distinction can be made easier by incorporating randomisation
(see Section 2.2.2 for further discussion).
Similarly, the assessment of toxicity for combination treatments should also be
addressed. Where the addition of an investigational therapy is expected to increase
both activity and toxicity to a potentially signicant degree, dual primary endpoints
may be considered to assess the ‘trade-off’ between greater activity and increased
toxicity (see Section 2.1.4 for further discussion).
2.1.1.4 Biomarker dependent
Biomarkers are an increasingly important part of clinical trials. They can be dened
as ‘a characteristic that is objectively measured and evaluated as an indicator of
normal biological processes, pathogenic processes, or pharmacologic responses to a
therapeutic intervention’ (Atkinson et al. 2001).
Biomarkers may be considered in the design of phase II trials in two ways.
First, a biomarker may serve as an outcome measure. The biomarker may be an
intermediate (primary) endpoint in a phase II trial provided it reects the activity of a
treatment and is associated with efcacy; this may form the basis for a stop/go decision
regarding a subsequent phase III trial. Decisions regarding the use of biomarkers as
primary outcome measures will feed into the decision regarding use of randomisation,
considering whether any historical data exist for the biomarker with the standard
treatment and the reliability of such data. Where a change in a biomarker reects
the biological activity of an agent, but is not predictive of the natural history of the
disease, this alone may be an appropriate endpoint for a proof of concept phase II trial;
in such cases a second, go/no-go phase IIb trial may be required to assess the impact
of the treatment on the cancer prior to a decision on proceeding to a phase III trial.
The use of biomarkers as outcome measures is discussed further in Section 2.2.1.
Second, in the era of targeted therapies a molecular characteristic of the tumour
that is relevant to the mechanism of action of the treatment under investigation may
serve as a biomarker to dene a specic subgroup of patients in whom an intervention
is anticipated to be effective. This has been done especially successfully in studies
of small molecules and monoclonal antibodies targeting HER-2 and related cell
surface receptors (Piccart-Gebhart et al. 2005; Slamon et al. 2001). The potential
16 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
for a biomarker to identify a subpopulation of patients may, however, only become
apparent after phase III investigation, as in the case of the monoclonal antibody
cetuximab in colon cancer where efcacy is limited to patients with no mutation in
the KRAS oncogene (Bokemeyer et al. 2009; Tol et al. 2009; Van Cutsem et al. 2009).
Where available, using a biomarker to enrich the population in a phase II trial in
this way can increase the likelihood of anti-tumour activity being identied, and thus
speed up drug development. By denition, when using a biomarker for population
enrichment, the resulting phase II population is not representative of the general
population. Interpreting outcomes in the enriched population may, therefore, be more
challenging as historical control data may be unreliable; randomisation incorporating
a control arm should be considered in such situations.
There are, however, potential risks with an over-reliance on biomarkers in phase
II trials. If the mode of action of a novel therapy has been incorrectly characterised,
the biomarker chosen for enrichment may be inappropriate and could lead to a
false-negative phase II trial because the wrong patient population has been treated.
Likewise, if a biomarker used to demonstrate proof of principle of biological activity
does not accurately reect the clinically relevant mode of action, the outcome of a
phase II trial may be misleading. When a biomarker is the primary endpoint for a trial
or used to enrich the patient population of patients it is vital that the biomarker be
adequately validated. Where there is insufcient evidence that a biomarker reliably
reects biological activity or identies an optimal patient group, measurement of
the biomarker in an unselected phase II trial population may be appropriate as a
hypothesis-generating exercise for future studies.
Approaches to trial design that incorporates biomarker stratication are discussed
further in Section 2.2.3.
2.1.2 Primary intention of trial
In this context, we dene the ‘intention’ of a trial not as the specic research question
but in the wider sense of classifying trials into two categories:
proof of concept, be that biological or therapeutic, or phase IIa;
go/no-go decision for further evaluation in a phase III trial, or phase IIb.
A proof of concept, or phase IIa, trial may be undertaken after completing a phase
I trial to screen the investigational treatment for initial evidence of activity. This may
then be followed by a go/no-go phase IIb trial to determine whether a phase III trial
is justied. Running two sequential phase II trials may, in some cases, be inefcient.
The Clinical Trial Design Task Force of the National Cancer Institute Investigational
Drug Steering Committee proposed that, where appropriate, proof of concept may be
embedded in a single go/no-go trial (Seymour et al. 2010).
A model that is increasingly relevant to the development of targeted anti-
cancer agents is to incorporate proof of concept translational imaging and/or
molecular/biomarker studies within the expanded cohort of patients treated at the
recommended phase II dose in a phase I trial. Where clear proof of concept can
KEY POINTS FOR CONSIDERATION 17
be demonstrated in this way, there is a blurring of the conventional divide between
phase I and IIa studies but the need remains for a subsequent phase IIb trial with the
intention of making a formal decision regarding further evaluation in a phase III trial.
While this specic point for consideration is not used to group the trial designs
given in Chapters 3–7, it is important in considering issues such as primary outcome
measures and the use of randomisation. Where a trial is designed as a proof of
concept study alone, it may be appropriate to conduct a single-arm trial to obtain
an estimate of the potential activity of a treatment to within an acceptable degree
of accuracy. Short-term clinical or biomarker outcomes may be appropriate to give
a preliminary assessment of activity prior to embarking on a larger scale phase IIb
study. Where the aim of the phase II trial is to determine whether or not to continue
evaluation of a treatment within a large-scale phase III trial, the ability to make formal
comparisons between experimental and standard treatments may be more appropriate,
to be more condent of that decision to proceed or not. Similarly, in phase IIb
trials outcome measures known to be strongly associated with the primary phase III
outcome measure are desirable for robust decision-making. Further discussion on the
choice of outcome measures and the use of randomisation is given in Sections 2.2.1
and 2.2.2, respectively.
2.1.3 Number of experimental treatment arms
Whereas historically phase II cancer trials invariably had a single-arm, an increasing
number now comprise multiple arms, one of which is often a ‘control’ standard
treatment arm. The most common randomised phase II cancer trial designs have a
single experimental arm with a control arm so the activity seen in the experimental
arm can be compared formally or informally with that seen in the control arm.
Randomisation may be appropriate where historical data on the outcome measure are
unreliable or when a novel agent is being added to an effective standard therapy (see
Section 2.2.2 for discussion).
Where multiple experimental treatments are available, or a single treatment that
may be effective using different doses or schedules, a phase II trial may be designed to
select which, if any, of these options should be taken forward for phase III evaluation.
Randomisation can also be used to evaluate multiple treatment strategies such as
the sequence of rst- and second-line treatments. In these settings assessment of
activity of each individual novel treatment, based on pre-specied minimal levels of
activity, can be assessed using treatment selection designs which are described in
Chapter 5.
Where multiple treatments are being investigated in a single phase II trial,
with each single treatment in a different subgroup of patients (e.g. treatment A
in biomarker-X-positive patients, and treatment B in biomarker-X-negative patients),
this should not be considered as a treatment selection trial since only one experi-
mental treatment is being investigated within each subgroup. For the purposes of
trial design, such trials fall under the ‘single experimental arm’ category. Further
discussion regarding trials of subgroups of patients is provided in Section 2.2.3.
18 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
2.1.4 Primary outcome of interest
The primary outcome of interest will depend on the existing evidence base and/or
stage of development of the treatment under investigation, its mechanism of action
and potential toxicity. Thus, information obtained from discussion of the therapeutic
considerations of the treatment is important in deciding the primary focus of the trial,
as well as incorporating data from previous studies of the same, or similar, treatments.
At this stage, for the purpose of categorising trial designs, the primary outcome of
interest is categorised as being either activity alone, or both activity and toxicity.
Designs are also available that address a third option, of considering toxicity alone as
the primary outcome measure in a phase II trial. These designs are incorporated with
those assessing both activity and toxicity and are described in Chapter 6. Discussion
regarding the specic primary clinical outcome measure is given in Section 2.2.1.
2.1.4.1 Activity
Where the toxicity of the investigational treatment is believed to be modest in the
context of phase II decision-making or the toxicity of agents in the same class is
well known, the primary phase II trial outcome measure will usually be anti-tumour
activity, with toxicity included amongst the secondary outcome measures.
2.1.4.2 Activity and toxicity (or toxicity alone)
If the toxicity prole of the investigational treatment, be it a single-agent or com-
bination therapy, is of particular concern, the activity and toxicity of the treatment
may be considered as joint primary outcome measures, such that the investigational
treatment must show both promising activity and an acceptable level of toxicity to
warrant further evaluation. Such designs allow incorporation of trade-offs between
pre-specied levels of increased activity and increased toxicity, to determine the
acceptability of a new treatment with respect to further evaluation in a phase III trial.
2.2 Stage 2 – Design components
2.2.1 Outcome measure and distribution
Emerging cancer treatments have many differing modes of action, which should be
reected in the choice of outcome measures used to assess their activity. While tumour
response according to Response Evaluation Criteria in Solid Tumours (RECIST)
(Eisenhauer et al. 2009) has historically been the most widely used primary outcome
measure, non-binary denitions or volumetric measures of response, measures of time
to an event such as disease progression or continuous markers such as biomarkers
may be more relevant when evaluating the activity of targeted or cytostatic agents
(Adjei et al. 2009; Booth et al. 2008; Dhani et al. 2009; Karrison et al. 2007; McShane
et al. 2009).
When choosing between the many possible primary outcome measures for a
phase II trial the key points to consider include the expected mechanism of action of
KEY POINTS FOR CONSIDERATION 19
the intervention under evaluation, the aim of treatment in the current population of
patients, whether there are any biomarker outcome measures available, the stage of
assessment in the drug development pathway (i.e. phase IIa or IIb) and the strength
of the association between the proposed phase II outcome measure and the primary
outcome measure that would be used in future phase III trials. The chosen outcome
measure should also be robust with respect to external factors such as investigator
bias and patient and/or data availability.
The primary outcome measure of a phase II trial should be chosen on the basis
that if a treatment effect is observed, this provides sufcient evidence that a treatment
effect on the phase III primary outcome is likely to be seen. The use of surro-
gate endpoints has been investigated in a number of disease areas, including breast
(Burzykowski et al. 2008), colorectal (Piedbois and Buyse 2008) and head and neck
cancer (Michiels et al. 2009). While the outcome measures used in phase II trials do
not need to full formal surrogacy criteria (Buyse et al. 2000) evidence of correlation
between the phase II and III outcome measures is important to ensure reliability in
decision-making at the end of a phase II trial.
The choice of primary outcome measures for a phase II trial reects the outcome
distribution. This section outlines the various options used to categorise phase II trial
designs within Chapters 3–7, according to the distribution of the chosen primary
outcome measure (as described in Chapter 1).
2.2.1.1 Binary
Response is usually evaluated via a continuous outcome measure, that is, the percent-
age change in tumour size. This is, however, typically dichotomised as ‘response’
versus ‘no response’ following RECIST criteria (Eisenhauer et al. 2009). Such binary
outcomes, categorised as ‘yes’ or ‘no’, may be used for any measure that can be
reduced to a dichotomous outcome including toxicity or change in a biomarker. Other
outcome measures that may be expressed as continuous, such as time to disease pro-
gression, are frequently dichotomised to reect an event rate, such as progression at
a xed time point.
In phase II studies of cytotoxic chemotherapy the biological rationale for response
as an indicator of anti-cancer activity is based in part on the natural history of
untreated cancers which grow, spread and ultimately cause death. Administration
of each cycle or dose of treatment kills a substantial proportion of tumour cells
(Norton and Simon 1977) and as such is linked to delaying death (Norton 2001).
These principles may be applicable to chemotherapeutic agents which target tumour
cell kill, and therefore the endpoint of response may be a relevant indicator of anti-
tumour activity.
There are inherent issues in the assessment of tumour response, associated with
investigator bias, inter-observer reliability and variation in observed response rates
over multiple trials (Therasse 2002). These may, to some degree, be alleviated by the
incorporation of independent central review of response assessments or the incor-
poration of a randomised control arm when historical response data are unreliable.
The use of classical response criteria for trials of drugs that may not cause tumour
20 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
shrinkage is likely to be inappropriate and raises questions over the design of phase
II trials and the endpoints being used (Twombly 2006). Measures of time to an event
such as disease progression or novel endpoints such as biomarkers may be more rel-
evant when evaluating the activity of newer targeted therapies. Nevertheless, because
most targeted or biological therapies are selected for clinical development on the
basis of pre-clinical data demonstrating at least some degree of tumour regression,
tumour response may remain an appropriate outcome measure for novel agents, as
acknowledged by two Task Force publications (Booth et al. 2008; Seymour et al.
2010).
2.2.1.2 Continuous
Continuous outcome measures such as tumour volume or biomarker response may
be appropriate and relevant outcome measures for consideration in studies of novel
agents (Adjei et al. 2009; Karrison et al. 2007; McShane et al. 2009). The use of
biomarkers in clinical trials is becoming increasingly common in the development
of targeted treatments with novel mechanisms of action. Only when a biomarker has
been validated as an outcome measure of activity, that is, when a clear relationship
has been established with a more conventional clinically relevant outcome measure,
should a biomarker be used as the primary outcome measure of a phase II trial.
The difculties in identifying validated biomarkers have been highlighted (McShane
et al. 2009), in addition to the need for technical validation and quality assurance
of the relevant assays. As discussed above, biomarkers may be dichotomised to
produce a binary outcome; statistical designs can, however, incorporate biomarkers
as a continuous outcome, which may often lead to more efcient trial design.
2.2.1.3 Multinomial
Multinomial outcome measures may offer an alternative to binary outcomes when
multiple levels of a clinical outcome may be of importance. For targeted or cytostatic
therapies, an alternative to binary tumour response (i.e. response vs. no response) that
remains objective may be the ordered categories of tumour response such as complete
response plus partial response versus stable disease versus progressive disease (Booth
et al. 2008; Dhani et al. 2009). Alternatively activity of an experimental therapy may
be evidenced by either a sufciently high response rate or a sufciently low early
progressive disease rate (Sun et al. 2009).
2.2.1.4 Time to event
Time to progression (TTP), time-to-treatment failure (TTF) or PFS may be considered
as appropriate outcome measures to assess the activity of treatments in phase II clinical
trials (Pazdur 2008).
TTP may be dened as the time from registration or randomisation into a
clinical trial to time of progressive disease;
KEY POINTS FOR CONSIDERATION 21
TTF may be dened as time from registration/randomisation to treatment dis-
continuation for any reason, including disease progression, treatment toxicity,
patient preference or death;
PFS may be dened as time from registration/randomisation to objective
tumour progression or death.
The use of these endpoints has increased in recent years as a means of assessing
the activity of targeted or cytostatic treatments, including cancer vaccines. While
TTP and PFS may better capture the activity of such agents, they do present their
own challenges. Trials incorporating TTP or PFS as the primary outcome measure
may be constrained by a lack of accurate historical time-to-event population data
with which to make comparisons. This limitation may be overcome by randomised,
comparative designs, but they inherently require larger sample sizes. TTP or PFS may
be inuenced by assessment bias in terms of frequency of assessment irrespective of
randomisation, highlighting the need to carefully consider the schedule of follow-up
assessments; increasingly, assessments are recommended at xed time points rather
than in relation to the number of cycles of treatment received to avoid such biases.
Additional time-to-event outcome measures may also be considered including, for
example, time to developing an SAE in trials primarily concerned with safety assess-
ment or time to a clinical event such as bone fracture in trials of drugs specically
acting against bone metastases.
2.2.1.5 Ratio of times to progression
One way to overcome the limitations of TTP and PFS as outcome measures with
regard to the challenges of unreliable historical data, and to avoid the need for
additional patient numbers in a randomised study, may be to use each patient as their
own control. The ratio of times to progression or ‘growth modulation index’ has been
proposed for trials in patients who have had at least one previous line of treatment
(Mick et al. 2000; Von Hoff 1998).
The growth modulation index (GMI) represents the ratio of the TTP on the current
investigational treatment relative to that on the previous line of ‘standard’ treatment,
that is, sequentially measured paired failure times for each patient. Although origi-
nally proposed in the 1990s, this outcome measure may be considered exploratory, as
it has not been widely used in phase II trials to date and relies on TTP data from the
previous line of treatment, the accuracy of which may be uncertain as it will usually
have been administered outside a clinical trial when assessments are less structured.
A GMI of 1.33 has been proposed as clinically relevant, but this threshold has not
been validated (Von Hoff 1998). Time-to-event ratios may, however, be worthy of
consideration as a phase II outcome measure where randomisation is not appropriate.
2.2.2 Randomisation
The use of randomisation in phase II trials is widely debated (Buyse 2000; Redman
and Crowley 2007; Yothers et al. 2006). Randomisation protects against selection bias,
22 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
balances treatment groups for prognostic factors and contributes towards ensuring a
valid comparison of the treatments under investigation, such that any treatment effect
observed can reasonably be attributed to the treatment under investigation and not
external confounding factors.
Although randomised phase III clinical trials provide the mainstay of evidence-
based clinical research, the use of randomisation within phase II is not so straight-
forward. Those opposed to randomisation in phase II trials argue that it can be
unacceptably restrictive from a resource perspective, as it inevitably requires at least
twice the number of patients (assuming 1:1 randomisation), increasing both the cost
and duration of the trial (Yothers et al. 2006). A further criticism is that where the
main purpose of randomisation is to balance for potential prognostic factors (of
which there may be many), this is unlikely to be achieved in randomised phase II
trials that are generally only modest in size (Redman and Crowley 2007). On the
other hand, those making the case for randomised phase II trials stress the inherent
problems of selection bias in uncontrolled trials (Buyse 2000). Therapeutic benets
are generally smaller than potential differences in outcome due to baseline patient
and disease characteristics; patient selection bias can, therefore, seriously confound
the interpretation of non-randomised phase II trials, and thus the decision to take a
treatment forward to phase III. This may not be a problem in a phase IIa trial of a
new cytotoxic that is simply screening to establish whether it has a pre-specied, and
often low, level of activity; bias is more of a challenge in a phase IIb trial where the
key question is whether a new treatment has a sufciently high level of activity to
warrant a large phase III trial.
For an increasing number of phase II studies, especially those of cytostatic or
targeted agents, where ‘traditional’ endpoints such as response rate are not likely
to be the most appropriate outcome measures, historical controls are problematic as
data for alternative endpoints such as PFS may not be available. Where such data do
exist, the population of patients on which the data are available must be considered
since patients entering phase II clinical trials will not be representative of the broader
patient population treated in routine practice from which historical outcome data
may be derived. It is, therefore, important that the patients from whom the historical
outcome data are derived are matched as closely as possible to the phase II population
in terms of baseline characteristics and disease biomarkers if used for enrichment.
If this is not possible, there is a strong argument to include randomisation against a
control arm within the phase II trial.
In the context of randomisation, another important point is whether the experimen-
tal therapy under investigation is to be delivered as a single agent or in combination.
Where an experimental therapy is given in combination with the current standard
treatment, it is very difcult to identify any additional activity of the experimen-
tal agent over and above that of the standard partner therapy unless a comparative
control arm is incorporated into the trial. Even if historical activity data do exist
for the standard therapy, patient selection and evolving patterns of patient care may
often render the interpretation of such data difcult. This should be considered in
detail when making the decision as to whether or not to incorporate a randomised
control arm.
KEY POINTS FOR CONSIDERATION 23
Although randomisation is increasingly being incorporated into phase II trial
design, it can take various forms. Simply because randomisation between experimen-
tal and control treatments is incorporated into a phase II trial does not automatically
imply that the two arms are formally statistically compared with sufcient power; the
reasons for randomisation should, therefore, be critically evaluated.
The statistical implications of conducting a single-arm or a randomised phase II
study have been evaluated in simulation studies. One study compared the results of
multi-centre single-arm and randomised phase II trials of the same sample size, where
the decision as to whether or not the experimental treatment was deemed successful
was based solely on it showing a higher response rate than in the historical control
population, or randomised control population, that is, no formally powered statistical
comparison was employed (Taylor et al. 2006). Where there was expected to be little
variability in response rates between centres, and both the variability and uncertainty
in the response rate for the control population were small, single-arm studies were
found to be adequate in terms of correct decision-making. However, with increased
variability and uncertainty in response rates for either the experimental or control
population, randomised studies were more likely to make the correct recommen-
dation regarding proceeding to phase III, and should be considered as a possible
option. A further study compared error rates between single-arm and randomised
comparative phase II trials, which reected more realistically the characteristics of
a phase II trial (Tang et al. 2010). Although sample sizes for the randomised tri-
als were at least double those of the single-arm trials, the false-positive error rates
(type I error) in single-arm trials were two to four times those projected when the
characteristics of the study patients differed from those of the historical controls;
by contrast, randomised trials remained close to the planned type I error. Statistical
power (type II error) remained stable for both designs despite differences in the patient
populations.
The impact of misspecication of the control data for either approach should be
considered in detail, for example, the impact of specifying a control response rate of,
say, 60% when in fact it may be as low as, say, 50%, or as high as 70%. In the single-
arm setting, the impact of such misspecication, potentially leading to increased
false-negative or false-positive results, is much higher than in the randomised setting
since there is no concurrent control arm against which to verify the initial control
assumptions made. Thus where there is uncertainty in the control data, the inclusion
of a control arm may be considered appropriate.
There is no one-size-ts-all approach to phase II trial design, and the theoretical
and practical implications of randomisation must be considered on a trial-by-trial
basis. Below we discuss the various randomisation options for phase II trial design
and provide examples of when each setting may be appropriate. Randomisation is
categorised within the thought process as
i. no randomisation (single-arm phase II trial);
ii. randomisation incorporating a control arm, no formally powered statistical
comparison intended;
24 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
iii. randomisation incorporating a control arm, formal comparison intended; and
iv. randomisation to multiple experimental treatments.
The use of randomised discontinuation designs is addressed separately in Section
2.2.3.
2.2.2.1 No randomisation
Chapter 3 outlines those designs that incorporate only a single experimental arm. The
results of most single-arm phase II trials are interpreted in the context of historical
control data. The reliability, or otherwise, of these historical data is one of the main
issues driving discussion about randomisation in phase II studies (Rubinstein et al.
2009; Vickers et al. 2007). Single-arm phase II designs have been reported that utilise
historical data but incorporate an estimate of potential variability arising from the
number of patients or trials from which those historical data have been derived, and
are presented in Chapter 3.
A single-arm study may be considered appropriate where
comparison with a control group is not relevant. For example, a phase IIa trial
designed to show proof of concept, where the intention is to obtain an initial
estimate of treatment activity to inform the design of a randomised phase IIb
trial;
the historical data are sufciently robust for the primary outcome measure as
to allow a reliable comparison, for example, a study of a single-agent cytotoxic
treatment with response rate as the primary outcome measure, conducted in
a broad population of patients with a common cancer refractory to standard
therapy.
2.2.2.2 Randomisation including a control arm
Randomisation including a control arm can be considered in two ways: randomisation
with no formal comparison between experimental and control arms and randomisation
with a formal comparison between experimental and control arms. Further discussion
of each of these is given below. Phase II trial designs incorporating randomisa-
tion between a single experimental therapy (or combination therapy) and a control
arm are presented in Chapter 4.
With no formal comparison
Those designs that incorporate a control arm with no formal comparison intended
as the primary decision-making assessment are highlighted in Chapter 4, as the
study is not designed to have sufcient power to detect statistically signicant dif-
ferences between treatment arms. This does not infer that a comparison may not be
made of outcomes between the arms; rather, that these comparisons be made with
the acknowledgement of reduced statistical power therefore providing additional
exploratory comparisons only. This approach may be appropriate if it is sufcient to
KEY POINTS FOR CONSIDERATION 25
simply show that the experimental treatment has activity within a certain range. Ran-
domisation provides a level of reassurance that the study population is representative
and guards against patient selection bias; this approach is more acceptable when at
least some historical data exist to further aid interpretation of the activity of the investi-
gational agent.
In the absence of formal comparison between treatment arms, the sample size may
simply be doubled compared to a single-arm study and decision-making at the end of
the trial based primarily on the results of the experimental arm, albeit in the context
of outcomes in the control arm. Data from the patients randomised to the control
arm can be more formally incorporated. For example, response rates in the control
arm may be compared to the historical control rates to determine whether they are
reective of the assumptions made when designing the trial (Buyse 2000; Herson and
Carter 1986).
It has been suggested that the use of a control arm as a reference arm only
should be avoided, particularly in trials of targeted or cytostatic agents, since it may
be difcult to interpret the results when unexpected outcomes are observed in the
control arm and when the sample size is not sufcient enough to permit direct formally
powered comparisons (Rubinstein et al. 2009). For example, if positive results were
observed in the experimental arm on the basis of pre-dened criteria, but higher than
expected activity was also observed in the control arm, does this call into question
the positive trial outcome? On the other hand, if the outcome of the experimental
arm is negative and the control arm also has a lower level of activity than expected,
should the apparent low activity of the experimental treatment be questioned? These
uncertainties may be mitigated by looking at both study arms in relation to appropriate
historical data, where available.
With formal comparison
When a phase II trial aims to determine more than whether the investigational agent
has activity within a broad range, or there are serious doubts about the accuracy
of historical control data, formal comparison between the control and experimental
treatment arms is preferred. The trade-off for increased reliability is inevitably a
larger sample size.
The level of statistical signicance within a comparative, randomised phase II
trial should be considered carefully as this will further impact on sample size. It is
acceptable to increase the type I error in a phase II trial compared to the typical 5%
level used within phase III trials, and error rates of up to 20% have been used. This
may enable more realistic sample sizes, and the error associated with incorrectly
declaring a non-active treatment worthy of further investigation in phase III (i.e. the
type I error) may be deemed more acceptable than that of incorrectly rejecting a
treatment that is active. It is, therefore, important to maintain the power associated
with the design of the trial.
Another consideration in the use of formally comparative phase II designs is
the feasibility of achieving the treatment difference that is being specied. While it
may be appropriate to target large treatment effects in some circumstances, this may
26 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
not be the case in others. The size of the clinically relevant treatment effect should,
therefore, be considered carefully to ensure the outcome assumptions are realistic
and not simply used as a method of reducing sample size.
It must also be stressed that with the use of formally comparative phase II designs,
a statistically signicant result at phase II would not usually obviate the need for a
subsequent phase III trial. In contrast to the short-term endpoints usually selected
in phase II trials, longer term endpoints such as PFS and OS are typically selected
in large-scale phase III trials. Additionally, in a relatively small randomised phase
II study only a limited number of patients will have received study drug so not all
clinically relevant toxicities may be identied and should therefore be studied further.
Information gained from the phase II trial may also inuence patient selection for
the denitive phase III study. Subsequent conrmatory phase III trials are, therefore,
usually required even after a statistically positive randomised phase II trial.
2.2.2.3 Randomisation to experimental arms (selection)
Where the aim of a phase II trial is to select which of several candidate investigational
treatments to take forward for further evaluation, randomisation may be incorporated
to randomise patients between several experimental treatments. Where historical
control data are either available or not relevant as discussed above, this will inuence
the decision as to whether or not a control arm is also incorporated, also as discussed
above.
2.2.3 Design category
Phase II statistical designs can be broadly separated into nine statistical design cate-
gories:
one-stage;
two-stage;
multi-stage (or group sequential);
continuous monitoring;
decision-theoretic;
three-outcome;
phase II/III;
randomised discontinuation; and
targeted subgroups.
These categories are not mutually exclusive. For example, a one-stage trial
may incorporate a three-outcome design, or analysis based on a decision-theoretic
approach. Where this is the case, designs have been listed according to their primary
design categorisation.
KEY POINTS FOR CONSIDERATION 27
A brief description of these nine categories is provided below, focusing on the
practical implementation of each design. Previous reviews have used alternative cat-
egories for phase II designs, focusing either on single-arm versus randomised studies
(Seymour et al. 2010) or specic designs such as randomised designs, enrichment
designs and adaptive Bayesian designs for trials of molecularly targeted agents only
(Booth et al. 2008). Mariani and Marubini also previously conducted a review of
the statistical methods available for phase II trials, categorising designs according to
one sample versus controlled, as well as according to the number of stages of assess-
ments, and focusing on a framework for trial analysis, that is, frequentist, Bayesian or
decision theoretic (Mariani and Marubini 1996). The grouping of trial designs within
this book adopts a similar design categorisation to Mariani and Marubini (Mariani
and Marubini 1996), but the thought process incorporates points for discussion prior
to making the specic design decision. Additional categories of design that Mariani
and Marubini (Mariani and Marubini 1996) did not consider are also included.
2.2.3.1 One-stage
A one-stage design utilises a xed sample of patients, recruited until the required
sample size is obtained. After the necessary follow-up of patients, analysis and
decision-making regarding proof of concept, whether to move to phase III or not, or
which treatment(s) to select to take forward to phase III, is made. One-stage designs
are relatively straightforward, avoiding complexities relating to recruitment strategies
if interim analyses are undertaken. They do not, however, allow for adaptations
such as early trial termination due to low levels of activity. Where the safety of a
treatment is well known, and data are already available to suggest activity, either for
a similar treatment or for the same treatment in an alternative population of patients,
a single-stage design may be appropriate since an interim ‘check’ may be deemed
unnecessary.
Where the experimental therapy is highly active, over and above the current
standard therapy, fewer patients may be required under a one-stage design than
other two- or multi-stage designs that incorporate early termination for lack of
activity only.
2.2.3.2 Two-stage
Under a two-stage design patients are recruited to the trial in two stages such that at
the end of the rst stage an interim analysis is performed and the trial may be stopped
for a number of reasons, including lack of activity, early evidence of activity or
unacceptable toxicity; otherwise, the trial continues to a second stage. Alternatively,
the interim analysis may be used to select which of several experimental treatments
to take forward to the second stage. Additional adaptations may be incorporated at
the end of the rst stage according to the specic trial design, for example, sample
size re-estimation.
Stopping rules are developed for each stage of the study to determine whether
to stop or continue, based on pre-specied operating characteristics relevant to the
28 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
specic trial and design. At the end of the study, data from both stages are typically
used in deciding how to proceed.
Two-stage designs are benecial in that the analysis at the end of the rst stage
may act as a ‘check’ on the treatment(s) under investigation, potentially exposing
fewer patients to an inactive treatment than would be exposed using a one-stage
design (i.e. under the null hypothesis of no treatment activity, two-stage designs may
be more efcient). There are, however, issues around patient recruitment while data
from the rst stage of the trial are being analysed. This is a particular issue if the
outcome of interest requires a substantial period of follow-up or observation, for
example, PFS requiring a specic number of events to be observed. During this time,
patients either continue to be recruited, therefore contributing to the second-stage
sample size, without the results of the rst stage being known, or recruitment is
suspended. Continuing recruitment avoids the trial losing momentum and may be
acceptable where recruitment is slow. If a trial is recruiting rapidly, however, the total
required number of patients may be entered to the second stage of the trial before the
rst-stage analysis is complete, rendering the two-stage design futile.
Careful thought must be given to these points when a two-stage design is being
considered. A compromise may be considered, which does not require a break in
recruitment but takes into account data from patients recruited during the follow-up
and analysis period of the rst-stage patients if required (Herndon 1998). If the rst-
stage analysis suggests stopping due to lack of activity, recruitment may be suspended
at this stage and an additional assessment performed incorporating data on all patients
recruited during the follow-up and analysis period to determine whether to stop the
trial permanently or to resume recruitment (see Chapter 3). Additionally, other two-
stage designs can be adapted when the attained sample size is different to the planned
sample size, especially if this results in over-recruitment, where the decision-making
criteria may be updated in line with the actual number of patients recruited (Chen
and Ng 1998; Green and Dahlberg 1992).
Due to the nature of two-stage designs, the total sample size requirement is not
xed, so a maximum sample size and an average sample number (ASN) are generally
specied, to account for possible early termination of the trial.
2.2.3.3 Multi-stage
Multi-stage designs, also known as group-sequential designs, are similar to the two-
stage designs described above, but with additional analyses throughout the course of
the trial. This allows more opportunities to terminate the trial, exposing fewer patients
to inactive and/or toxic treatments, or to accelerate the start of the phase III trial
through early termination of the phase II trial if sufcient evidence of activity is seen.
Additional adaptations may be incorporated at each stage, and again stopping rules are
developed for each stage of the study based on pre-specied operating characteristics
relevant to the specic trial. As with two-stage designs, multi-stage designs require
consideration of whether to continue recruitment whilst interim assessments are
underway. Again, due to the nature of multi-stage designs, a maximum sample size
and an ASN are generally specied.
KEY POINTS FOR CONSIDERATION 29
Two-stage or multi-stage designs are generally chosen because of their ability
to terminate a trial earlier than a xed sample one-stage design and may be seen to
allow a more cautious approach. This is useful when the activity of a treatment is
not known and/or toxicity is likely to be considerable. In this case a two-stage or
multi-stage design may be appropriate irrespective of the implications of continuing
or suspending recruitment. Where such caution is not necessary, a two-stage or multi-
stage design may not be appropriate, especially when either suspending or continuing
recruitment is problematic; in these cases, a one-stage design may be an alternative.
2.2.3.4 Continuous monitoring
With continuous monitoring designs the outcome of interest is assessed after each
individual patient’s primary outcome has been observed. The rationale behind this
design is generally to allow early termination of the trial in case of lack of activity. This
provides, therefore, a more cautious approach to trial design, allowing termination
as soon as possible rather than waiting for a pre-dened number of patients to
be recruited. Again, pre-dened stopping rules are required and determined via
the specic design operating characteristics. Recruitment may continue while the
outcomes are observed, but real-time reporting of outcomes is fundamental to this
design. This is not possible if the primary outcome requires a prolonged period of
observation as with PFS or even best response, both of which may not be available
for some months following the start of treatment. In such cases there is little to be
gained from continuous monitoring over a multi-stage design, since many additional
patients may be recruited before data from the ‘last’ patient can be analysed. This may
be less problematic where, for example, acute treatment toxicity is the key outcome
measure, as this can generally be assessed more quickly than activity.
2.2.3.5 Decision-theoretic
Decision-theoretic designs consider costs and gains associated with making incorrect
decisions at the end of the phase II trial and incorporate utility functions associated
with these costs and gains. Variables such as the total patient ‘horizon’, that is,
the likely number of patients who would be treated with an effective new drug as
standard therapy after completion of a successful phase III trial, before the next new,
more effective drug becomes available, are required (Sylvester 1988). If that patient
population is small, and especially if the likely cost of the new treatment high, there
may be a nancial imperative that the magnitude of the treatment effect sought in
the phase II study be large. Decisions are generally made at the end of the trial after
a xed number of patients have been recruited, as in a one-stage design, although
multi-stage designs may also be used. These designs allow decisions to be made based
on an all-round assessment of gain, as opposed to concentrating solely on clinical
activity (Sylvester and Staquet 1980).
Although the ability to incorporate information regarding costs and gains
associated with decisions made throughout a trial is clearly potentially useful,
decision-theoretic designs are rarely used in phase II oncology trials. This may reect
30 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
the difculty of identifying accurately the cost and patient horizon information that is
often required for their design and difculty in formulating realistic and interpretable
models.
2.2.3.6 Three-outcome
The three-outcome design may be seen as a sub-design of the one-, two- or multi-stage
designs (Storer 1992). The main characteristic of this design is that instead of there
being two possible outcomes at the end of the phase II trial, that is, reject the null
hypothesis or reject the alternative hypothesis, a third outcome is incorporated where
the trial is inconclusive on the basis of primary endpoint data. This approach may
be used when there is a region of uncertainty between, for example, a response rate
above which further investigation in phase III is warranted and a response rate below
which it is not. If the primary outcome measure data of such a trial are inconclusive,
the decision to move to phase III or not may be based on alternative outcome measures
such as safety or cost.
Three-outcome designs may be single arm or randomised. Upper and lower
stopping boundaries are developed for each stage of the study to determine whether
to stop, continue or declare the trial inconclusive. To calculate these boundaries,
in addition to the conventional type I and type II errors (𝛼and 𝛽, respectively),
two further errors must also be considered. The probability of correctly making
the decision to reject the null hypothesis when the alternative is true (𝜋) and the
probability of correctly making the decision to reject the alternative hypothesis when
the null is true (𝜂), are required. With these four errors specied one can then
determine the probability of incorrectly declaring an inconclusive result when in fact
the alternative hypothesis is true (𝛿) and the probability of incorrectly declaring an
inconclusive result when in fact the null hypothesis is true (𝜆). These differing error
rates are shown graphically in Figure 2.2, assuming a binary outcome measure of
success or failure. Here, nLand nUrepresent the lower and upper stopping boundaries,
respectively, for the number of successes observed; Nis the sample size; and p0and
p1represent the success rates associated with the null and alternative hypotheses,
respectively. Alternatively, the probabilities of concluding in favour of either the null
or alternative hypotheses when in fact the true response rate lies within the region of
uncertainty may be specied (Storer 1992).
Three-outcome designs generally require fewer patients than a typical two-
outcome design using the same design criteria. They may also be seen as better
reecting clinical reality than the typical two-outcome design where the decision
λ
ηπ
nL
α
β
δ
Np1
Np0nU
Figure 2.2 Probabilities associated with the three-outcome design.
KEY POINTS FOR CONSIDERATION 31
between accepting and rejecting the null hypothesis may be determined by a single
success or failure (i.e. a single stopping boundary).
2.2.3.7 Phase II/III
Phase II/III designs are used when the transition from phase II to III is required to be
seamless. Such designs typically allow data generated from the phase II component of
the trial to be incorporated in the nal phase III analysis. These trials are, therefore,
usually randomised, incorporating a control arm. Randomisation may be used to
select the ‘best’ of several treatments in the phase II component to be carried forward
into phase III or to decide whether or not to continue an individual experimental
treatment (single-agent or combination therapy) into the phase III component.
One of the main benets of these designs is that they reduce the total time required
for the study to progress through phases II and III compared to running separate trials.
Since data from the phase II component may also be incorporated in the phase III
analysis, patient resources are also reduced; this is a major benet in rarer cancers
or disease sub-types where the patient population is small. Where a limited number
of patients are available for trial recruitment, the optimal use of patient data is even
more crucial than usual.
Alternatively, where separate phase II and phase III trials are to be carried out, a
phase II trial allowing early termination for evidence of activity may be considered
appropriate, bringing forward the phase III trial and saving patient resource. Where
patient numbers are limited, various trial design scenarios should be investigated
to identify the design which is most efcient in terms of patient numbers whilst
providing sufciently robust results.
As with multi-stage designs, the issue of whether to continue or suspend recruit-
ment during the analysis of the phase II component arises. The trial risks losing
momentum if recruitment is suspended, but rapid recruitment during this period may
result in a substantial number of patients being entered into the phase III element,
rendering the phase II/III design futile in its attempts to reduce the number of patients
exposed prior to embarking on phase III. For these trials to be carried out successfully,
the funding body, be it academic or industry, must commit to the full phase II/III
package in the knowledge that the trial may terminate after the phase II component.
Often many more centres will participate in the phase III component than in the phase
II. Since the trial may terminate for lack of activity after the phase II component, the
early preparation of centre set-up to enable a smooth transition to phase III must be
weighed against this possibility of early termination.
Specic phase II/III designs are outlined in Chapters 3–7. An alternative approach
to designing a phase II/III trial is to use conventional stand-alone phase II designs to
make decisions as to whether to continue to phase III or not and incorporate these
into phase III seamlessly (Storer 1990). Typically in this case, the primary outcome
measure under investigation during phase II is different to the primary outcome
measure under investigation during the phase III component, to avoid the need to
adjust the type I error rate in the phase III component. Otherwise, when the same
outcome measure is used in both phases II and III, with a formal comparison between
control and experimental treatments at the end of phase II, this essentially becomes
32 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
a phase III trial with at least one interim analysis. This approach, using the same
endpoints, is not generally recommended since the phase III endpoint will usually
be a long-term outcome such as OS. A long follow-up period would, therefore, be
required for that endpoint to be assessed in the interim/phase II analysis. Multi-stage
approaches may, however, be based on the phase II outcome measure with subsequent
interim analyses within the phase III component based on the phase III endpoints.
Phase II/III designs are inevitably associated with patient and resource efcien-
cies, accelerating the transition between the two trial phases, and usually allowing
patients recruited to phase II to be incorporated in the phase III analysis. However, by
performing separate phase II and phase III trials the results of the phase II trial, and
lessons learned during its set-up and conduct, may be incorporated into the design of
the phase III trial. Changes to eligibility criteria or follow-up schedules, for example,
may be required for the phase III trial. Here separate phase II and III trials enable
these alterations to be made. Such an approach to the planning of current and future
trials may be benecial where experience with an experimental treatment is minimal,
or data for the control population of the disease area in question are minimal, enabling
additional learning between stages of the development pathway.
2.2.3.8 Randomised discontinuation
Randomised discontinuation, or enrichment, trial designs (Kopec et al. 1993; Rosner
et al. 2002; Stadler 2007) involve all study patients initially being treated with the
experimental treatment for a pre-dened period of time, and then assessed for response
to treatment. Typically, those with progressive disease come off study whilst patients
who are responding continue to receive the experimental agent; those with stable
disease are randomised to either continue the experimental treatment or discontinue
it and either remain off treatment or receive standard treatment depending on the
question being asked in the trial.
Such an approach may be appropriate when the specic population of patients
in which the experimental treatment is expected to be effective is unknown. For
example, when evaluating a targeted agent where the level of expression of the relevant
target required for potential activity is not known, a randomised discontinuation
design may allow de facto enrichment of the patient population. Against this, only a
limited proportion of the population recruited to the trial actually contributes to the
randomised part of the study. An overview of the randomised discontinuation design
is presented by Stadler, providing an example of where the design has been used
successfully, as well as providing a summary of the advantages and disadvantages of
the design (Stadler 2007).
The role of the randomised discontinuation design has been reviewed in detail
(Booth et al. 2008; Capra 2004; Freidlin and Simon 2005; Kopec et al. 1993; Rosner et
al. 2002; Rubinstein et al. 2009). The Methodology for the Development of Innovative
Cancer Therapies (MDICT) Task Force (Booth et al. 2008) considered the design as
being exploratory in nature due to lack of clarity on its role in oncology. One study
comparing the randomised discontinuation design with a comparative randomised
design showed that the randomised discontinuation design may be underpowered
in comparison to the traditional design due to the number of patients who start the
KEY POINTS FOR CONSIDERATION 33
investigational treatment who are not then randomised (Capra 2004). An accurate
estimate of the proportion of patients likely to enter randomisation is, therefore,
essential in planning the study sample size. By contrast, a second study concluded
that, although the randomised discontinuation design may be less efcient than the
classical randomised design in many settings, it can be useful in the early development
of targeted agents where a reliable assay to select patients expressing the target is not
available (Freidlin and Simon 2005). The randomised discontinuation design may
be especially appropriate when treatment benet is restricted to a select group of
patients who are not identiable at the start of the trial.
2.2.3.9 Targeted subgroups
In the era of targeted therapies it may be appropriate to investigate the activity of a
treatment in a specic subgroup of patients in whom the intervention is anticipated to
be effective. Alternatively, where the specic subgroup of patients is not determined,
or there is uncertainty about whether a biomarker accurately identies a ‘sensitive’
patient population, it may be appropriate to assess activity simultaneously in several
subgroups of patients according to biomarker characterisation. Population enrichment
for a specic biomarker in phase II trials was discussed in Section 2.1.1.4, highlighting
the risks associated with incorrect characterisation and the possibilities of false-
negative results, as well as issues surrounding the use of historical data.
Designs that incorporate subgroup stratication may be used to enable the
inclusion of separate cohorts of patients dened by the biomarker in question, or
populations dened by other disease sub-types or patient characteristics, ensuring that
adequate numbers are recruited into each cohort. Approaches incorporating stratica-
tion range from separate phase II trials within each stratication level, to hierarchical
Bayesian designs (Thall et al. 2003) and tandem two-stage methods where the exper-
imental treatment is rst tested in an unselected patient population, and if there is
insufcient activity in this overall group, the trial continues in a select population
(e.g. marker-positive patients) only (Pusztai et al. 2007). Trials may also be partially
enriched to include a larger proportion of, for example, biomarker-positive patients,
providing additional power to detect treatment effects in this targeted subgroup of
patients.
These designs are discussed in Chapter 7; however, as noted in Chapter 1, a
number of recent papers have been published on biomarker stratication (An et al.
2012; Buyse et al. 2011; Freidlin et al. 2012; Freidlin and Korn 2013; Jenkins et al.
2011; Lai et al. 2012; Mandrekar et al. 2013; Roberts and Ramakrishnan 2011;
Tournoux-Facon et al. 2011), therefore we encourage consideration of additional
literature outlining alternative designs available.
2.3 Stage 3 – Practicalities
2.3.1 Practical considerations
At this stage of the design process, when faced with a number of statistical designs
from which to choose, deciding which particular design is most appropriate to your
34 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
particular setting can be difcult. Although all the designs considered are deemed
easy to implement, this section focuses on key practical aspects.
2.3.1.1 Programming requirements
For each statistical design described in Chapters 3–7, the programming require-
ments have been considered. It is important that the statistical methodology can be
implemented easily and efciently, allowing the statistician to consider various trial
scenarios during the design process. Only those designs that detail availability of
programs, or for which sufcient information is provided to allow the design to be
implemented, have been incorporated in this book. Nevertheless, some designs may
still be easier to implement than others depending on the resources available.
2.3.1.2 Availability/robustness of prior data
It is essential to consider the design parameters that must be dened in order to
implement each design and the variability associated with each of these parameters.
There may be, for example, a paucity of data on the primary outcome measure for
patients receiving the current standard treatment in the particular trial setting, so what
is the impact of those historical data being unreliable? For example, if the response
rate with standard therapy is estimated to be 20%, a phase II study may aim for a
response rate of 30% with the experimental arm. If a randomised phase II trial is
powered on such a basis but the patients in the control arm have better outcomes than
expected, the study may be underpowered. On the other hand, if a single-arm study is
undertaken and the historical response rate is overestimated an active treatment may
be inappropriately discarded. The implication of misspecication of study parameters
may be investigated by simulation or may already be addressed within the specic
design publication. If a trial design is not robust with regard to misspecication, it
may be more appropriate to consider a design that allows either an estimate of the
variance of the parameter to be incorporated into the design or to select a different
outcome measure that is robust in the face of misspecication. Additionally there may
be a specic design parameter for which no reliable data are available, for example, an
estimate of the correlation between a change in a biomarker and a clinically relevant
outcome measure. Here it may be possible either to consider a design that does not
require the parameter in question or to simulate the performance of the design under
differing parameter assumptions.
2.3.1.3 Early termination
Typically in phase II trials, early termination of a trial is incorporated to ensure
the safety and appropriate treatment of patients, usually in the context of lack of
activity or unacceptable toxicity. Early termination when evidence of activity has
been demonstrated may not be deemed necessary given the desire to obtain as much
information on the treatment as possible to provide a more robust estimate of the
treatment’s activity to inform the design of subsequent trials. On the other hand,
KEY POINTS FOR CONSIDERATION 35
this may delay opening of subsequent phase III trials and there are designs that do
incorporate early termination for activity.
2.3.1.4 Operating characteristics
In phase II trials, a larger type I error than typically used in phase III trials (e.g. 5%
two sided) is generally accepted due to the nature of phase II trials. Type I errors
of up to 20% have been used where the consequences of incorrectly rejecting an
active treatment are deemed less acceptable than those of inappropriately continuing
to develop a treatment that will ultimately not be active. In such circumstances,
subsequent larger phase III studies would be expected to identify the treatment as
inactive, whereas if a treatment is rejected the situation may well not be remedied as a
phase III trial is unlikely. The selection of an appropriate type I error rate is, therefore,
crucial to the reliability of the trial results and the efcient development of new
treatments. While larger type I error rates allow smaller sample sizes, investigators
need to consider carefully whether it is appropriate to conduct a small phase II study
with a high risk of a false-positive result, and ‘negative’ subsequent phase III trial;
the alternative is a larger phase II study with a lesser chance of development of an
ultimately ‘negative’ phase III trial.
Since the primary aim of many phase II trials is to determine whether a treatment
has a pre-specied level of activity, the power of phase II studies should generally
remain high; in practice, this means a power of at least 80%.
2.4 Summary
This chapter provides guidance on decision-making when identifying a trial design
for a phase II trial (Figure 2.1). Clinical researchers and statisticians should consider
carefully each of the three stages of the thought process; additional resources should
be consulted where necessary, and discussion maintained between the clinician and
statistician. The guidance we offer is not intended to be exhaustive or proscriptive.
Further reading and discussion around specic areas relevant to each specic phase
II design element should always be encouraged.
Examples of using the thought process in practice are presented in Chapters 8–12
for various trial design scenarios. These are intended as practical examples of how
the thought process may be implemented.
3
Designs for single experimental
therapies with a single arm
Sarah Brown
3.1 One-stage designs
3.1.1 Binary outcome measure
Fleming (1982)
One-stage, binary outcome
Standard software available
Fleming proposes a one-stage, two-stage and multi-stage design requiring spec-
ication of response rates under the null and alternative hypotheses and type I and
II error rates. Decision criteria are based around rejecting the null hypothesis that
the response rate of the experimental treatment is not less than some pre-specied
response rate, typically dened as the expected response rate of the current historical
control treatment. Sample size is based on normal approximation to the binomial
distribution. This is a widely used design and programs are readily available (e.g.
Machin et al. 2008).
Fazzari et al. (2000)
One-stage, binary outcome
Requires programming
A Practical Guide to Designing Phase II Trials in Oncology, First Edition.
Sarah R. Brown, Walter M. Gregory, Chris Twelves and Julia Brown.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
DESIGNS WITH A SINGLE ARM 37
Fazzari and colleagues propose modications to previously published phase II
designs. The modications include: incorporating a patient population that is more
representative of the intended phase III trial population, by reducing the eligibility
restrictions and increasing the number of centres; increasing the sample size to
allow more accurate estimates of the treatment activity; using an outcome measure
that is more representative of that to be used in phase III, recommending a k-year
progression-free survival (PFS) or overall survival (binary) outcome measure for
advanced-stage disease populations; taking the upper limit of the 75% condence
interval of the activity of previous treatments as the minimum activity required to be
observed to warrant moving to phase III. The methodology for the design of the study
is based on rejecting the minimum activity required from an x% condence interval
around the estimate of treatment activity with, say, 80% probability. Sample size is
generated using Monte Carlo simulation which will require programming.
A’Hern (2001)
One-stage, binary outcome
Standard software available
A’Hern presents an adaptation of Fleming’s design (Fleming 1982). Calculation
of sample sizes and cut-offs is based on exact binomial distributions as opposed to
normal approximation. Trials based on exact distributions are typically larger than
those using the normal approximation; however, they avoid the possibility that con-
dence intervals around the estimate of activity at the end of the trial will incorrectly
contain the lower rejection proportion due to approximation to the normal distribu-
tion. As for Fleming, this design is widely used and programs are readily available
for its implementation. The choice between Fleming and A’Hern should be based on
the sample sizes and the requirement for exact testing.
Chang et al. (2004)
One-stage, binary outcome
Pascal programs noted as being available from authors
Chang and colleagues propose a design whereby the sample size, and thus the
test statistic, is calculated using exact unconditional methods. This design may be
used when the historical control data are based on only a few patients (say up to
120). The number of patients on which the historical data are based is required to be
known as analyses take into account the pooled variance of the historical control and
experimental data. Tables and software are available to calculate sample sizes.
Mayo and Gajewski (2004)
One-stage, binary outcome
Requires programming
38 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Mayo and Gajewski propose sample size calculations for a single-arm single-stage
trial with binary outcome, using Bayesian informative priors (pessimistic/optimistic).
This is an extension of the two-stage designs proposed by Tan and Machin (2002).
Prior information regarding expected response rate and level of uncertainty in this
value is required to determine sample sizes using either the mode, median or mean
approach. Programming is required for the median and mean approaches, possible in
Matlab. Sample sizes will vary depending on the approach used.
Gajewski and Mayo (2006)
One-stage, binary outcome
Requires programming
Gajewski and Mayo describe Bayesian sample size calculations where conicting
opinions on prior information can be incorporated. Information required to design
the trial includes prior distributions, cut-off for the posterior probability that the true
response rate is greater than some pre-specied value and an error term relating to a
small increase in the target response rate. Sample size calculation is iterative; therefore
some computation is required to identify the design characteristics, for which no
software is detailed but for which formulae are given to enable implementation.
This design differs from the earlier design proposed by Mayo and Gajewski (2004)
as it allows incorporation of pessimistic and optimistic priors, as opposed to one
informative prior.
Vickers (2009)
One-stage, binary outcome
Stata programs given in appendix to manuscript
Vickers proposes a design using historical control data to generate a statistical
prediction model for phase II trial. The observed trial data for the experimental
arm are then compared to the predicted results to give an indication of whether
patients treated with the experimental agents are doing better than expected, based
on the prediction model. The authors note that the methodology hinges on quality
historical control data relevant to the patient population under study. Step-by-step
methodology is presented which incorporates bootstrapping on both the historical
data set and the observed data set and a comparison of the predicted and actual
outcomes. Example Stata code is given in the appendix to the manuscript to allow
implementation of the statistical analysis, as well as assessment of power.
3.1.2 Continuous outcome measure
No references identied.
DESIGNS WITH A SINGLE ARM 39
3.1.3 Multinomial outcome measure
Zee et al. (1999)
One-stage, multinomial outcome
Requires programming
Zee and colleagues propose single-stage and multi-stage single-arm designs con-
sidering a multinomial outcome, in the context of incorporating progressive disease
as well as response into the primary outcome measure. Analysis is based on the num-
ber of responses and progressions observed, compared with predetermined stopping
criteria. A computer program written in SAS identies the operating characteristics
of the designs. This is not noted as being available in the paper; however, detail is
given to allow implementation.
Lu et al. (2005)
One-stage, multinomial outcome
Programs may be available from authors
Lu and colleagues propose a design (one-stage or two-stage) to look at both
complete response (CR) and total response (or other such outcome measures whereby
observing one outcome implies the other outcome is also observed). The design
recommends a treatment for further investigation if either of the alternative hypotheses
is met (i.e. for CR or for total response) and rejects the treatment if neither is met.
The designs follow the general approach of Fleming’s single-stage (Fleming 1982)
or Simon’s two-stage (Simon 1989) approach whereby the number of CRs and total
responses are compared to identied stopping boundaries. Tables are provided for
some combinations of null and alternative hypotheses; however, formulae are given
and at the time of manuscript publication programs were in development. The design
differs from others in this section in that one outcome measure is a sub-outcome
measure of the other, whereas other designs consider discrete outcome measures
such as partial response (PR) versus CR.
Chang et al. (2007)
One-stage, multinomial outcome
Programs noted as being available from authors
Chang and colleagues propose a single-stage and a two-stage design for window
studies which aim to assess the potential activity of a new treatment in newly diag-
nosed patients. Treatment is given to patients for a short period of time before
standard chemotherapy, and each patient is assessed for response or early pro-
gression (both binary outcome measures). The alternative hypothesis is based on
40 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
both the response rate being above a pre-specied rate and the early progressive
disease rate being below a pre-specied rate. The outcomes follow a multino-
mial distribution. A SAS program is noted as being available from the authors to
identify designs.
Stallard and Cockey (2008)
One-stage, multinomial outcome
Programs noted as being available from author
Stallard and Cockey propose single-arm, one- and two-stage designs for ordered
categorical data, where the rejection region for the null hypothesis is dened based
on the likelihood ratio test. The null region over which the type I error is controlled
considers a weighting of the proportion of patients in each response category, in a
similar manner to that of Lin and Chen (2000). The focus of the paper is on response
with three levels; however, the design may be extended to more than three levels.
Programs are noted as being available from the rst author to allow identication of
designs.
3.1.4 Time-to-event outcome measure
No references identied.
3.1.5 Ratio of times to progression
Mick et al. (2000)
One-stage, ratio of times to progression
Requires programming
Mick and colleagues propose a design based on the growth modulation index
(ratio of time to progression of experimental treatment relative to that on the patients’
previous course of anti-cancer treatment). The outcome measure is novel and the
authors justify its use for trials of cytostatic treatments where outcome measures such
as tumour response may not be appropriate. Various values of the growth modulation
index for null and alternative hypotheses should be considered to explore design
parameters, as appropriate for the setting of the study. Each patient acts as their
own control. Information is required for each patient on their time to progression on
previous treatment, and an estimate of the correlation between the two times is needed.
The design is identied via simulation, which allows investigation of the effect of the
correlation estimate on the overall design. Although software is not detailed as being
available, this has been implemented in Splus, and detail is provided to allow design
implementation.
DESIGNS WITH A SINGLE ARM 41
3.2 Two-stage designs
3.2.1 Binary outcome measure
Gehan (1961)
Two-stage, binary outcome
Standard software available
Early termination for lack of activity
Gehan proposes one of the earliest designs to assess experimental treatments
in phase II trials. The methodology is based on the double sampling method and
considers a phase II trial composed of a ‘preliminary’ stage and a ‘follow-up’
stage. The preliminary stage assesses whether the treatment under investigation is
likely to be worth further investigation, using a condence interval approach to
exclude treatments with response rates below those of interest from further investi-
gation. The follow-up stage assesses the activity of the treatment with pre-specied
precision. The number of patients to be included in the follow-up stage is deter-
mined according to the number of responses observed during the rst stage. The
proposed design is intended to completely reject inactive treatments quickly, such
that if the response rate of interest is excluded from the condence interval at the
end of the rst stage, the trial is terminated early. Otherwise the trial continues.
In the second stage the activity of the treatment is estimated with given preci-
sion, rather than providing decision criteria for continuing to a further trial. On
this basis, this design may be seen as an estimation procedure for initial proof
of concept trials rather than trials to determine whether or not to proceed to
phase III.
Fleming (1982)
Two-stage, binary outcome
Standard software available for overall sample size
Early termination for activity or lack of activity
Fleming proposes a one-stage, two-stage and multi-stage design. The multi-stage
design addresses multiple testing considerations to allow early termination in case
of extreme results, employing the standard single-stage test procedure at the last
test. Tables are presented for specic design scenarios using the exact underlying
binomial probabilities rather than the normal approximation to these probabilities.
Programs are readily available to calculate the overall sample size for a one-stage
design (e.g. Machin et al. 2008), with sample sizes at each stage chosen to be approx-
imately equal. Termination at the end of each stage is permitted for activity or lack
of activity.
42 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Simon (1987)
Two-stage, binary outcome
Requires programming
Early termination for lack of activity
Simon introduces a two-stage design that is single arm with a binary outcome
whereby the sample size is minimised under a pre-specied expected response rate,
not necessarily the null or alternative response rate. Where this expected response rate
corresponds with the null hypothesis response rate, this design is equivalent to the
optimal design proposed in the subsequent paper summarised below (Simon 1989).
The current design is optimised by keeping the size of the rst stage small, making
the probability of rejecting an inactive drug high, and not allowing too high a sample
size in the second stage. Early termination is permitted at the end of stage 1 only
for lack of activity. A table is provided with limited design scenarios; however, the
designs detailed below (Simon’s optimal and minimax) are more widely used and
may be considered ahead of this earlier design.
Simon (1989)
Two-stage, binary outcome
Standard software available
Early termination for lack of activity
Simon proposes a single-arm two-stage design based on minimising the expected
number of patients under the null hypothesis (optimal), as well as an additional
design that minimises the maximum sample size (minimax). This is a well-known
and widely used two-stage design, based on null and alternative response rates, power
and signicance level, and the observed number of responses at the end of each stage
is used to assess stopping rules. The outcome of interest is binary and the trial may
only be terminated at the end of the rst stage for lack of activity. Extensive tables are
provided for different design scenarios and software is readily available (e.g. Machin
et al. 2008).
Green and Dahlberg (1992)
Two-stage, binary outcome
Requires programming
Early termination for lack of activity
The design described by Green and Dahlberg permits early termination for lack
of activity at the end of stage 1 when the alternative hypothesis is rejected at the 0.02
signicance level. At the end of the second stage a signicance level of 0.055 is used to
reject the null hypothesis and declare sufcient activity for further investigation. Some
DESIGNS WITH A SINGLE ARM 43
detail is given regarding stopping boundary and sample size calculation, although this
would need to be programmed and solved iteratively to nd the most suitable design.
This paper also discusses adaptations to the designs of Gehan (1961), Fleming (1982),
and Simon (1989), in the cases where the nal attained trial sample size differs from
the original planned design.
Heitjan (1997)
Two-stage, binary outcome
Programs noted as being available from the author
Early termination for activity or lack of activity
Heitjan proposes a design whereby decision-making is based on the ability to
persuade someone with extreme prior beliefs that the treatment under investigation
is either active or not. This requires specication of extreme priors. For a sceptic,
the probability that the experimental treatment is better than the standard treatment
must be at least some pre-specied value (e.g. 70%) for the treatment to be declared
active (known as the ‘persuade the pessimist probability’ PPP), and for an enthusiast,
the probability that the experimental treatment is worse than the standard treatment
must be at least some pre-specied value (e.g. 70%) for the treatment to be declared
inactive (known as the ‘persuade the optimist probability’ POP). Timing of interim
analyses can either be based on numbers of patients or time during the trial. Sample
size is justied by assessing the operating characteristics and calculating PPPs and
POPs of the design under various scenarios. Programs are noted as being available
upon request from the author. Early termination is permitted for activity or lack of
activity.
Herndon (1998)
Two-stage, binary outcome
Requires programming
Early termination for lack of activity
Herndon proposes a hybrid two-stage design that allows continuation of recruit-
ment while the results of the rst stage are being analysed. If the results of the rst
stage indicate the treatment is inactive, accrual is suspended and data are re-analysed
including data from all patients recruited to that time point. Otherwise, the design
continues to target recruitment for the second stage. The sample sizes for the rst
and second stages are chosen for practicality rather than via Simon’s optimal method,
with overall sample size calculated to maintain pre-specied type I and II errors for
study-specic null and alternative hypotheses. Critical values for suspending recruit-
ment, reinitiating or terminating recruitment and for declaring the treatment worthy
of further investigation at the end of stage 2 are calculated. To identify the critical
values a numerical search is required, for which formulae are provided. If the stage
44 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
I results indicate re-analysis using all patients to that time point, analysis follows
similar methodology to that proposed by Green and Dahlberg (1992), detailed above,
as does the analysis of stage II.
Chen and Ng (1998)
Two-stage, binary outcome
Programs noted as being available from authors
Early termination for lack of activity
Chen and Ng propose a exible design that operates in the same manner as
Simon’s two-stage design (Simon 1989), but here the number of patients at the rst
and second stages can vary by up to eight patients to allow a period of grace in
halting recruitment (in a similar manner to that described by Green and Dahlberg
1992, detailed above). A FORTRAN program is noted as being available from the
authors to enable implementation, and tables are given for some scenarios.
Chang et al. (1999)
Two-stage, binary outcome
Requires programming
Early termination for activity or lack of activity
Chang and colleagues outline a design for continuous or binary outcomes that
takes into account the number of patients on whom historical control data are based.
This reects the fact that the variances of the historical control data and the experi-
mental data will differ. The trial may be terminated at the end of the rst stage for
either activity or lack of activity. Algorithms are used to determine critical values for
stopping, and sample size is calculated by multiplying the single-stage sample size
(formulae provided) by between 1.02 and 1.05.
Hanfelt et al. (1999)
Two-stage, binary outcome
Programs noted as being available from authors
Early termination for lack of activity
Hanfelt and colleagues propose a modication to Simon’s two-stage design
(Simon 1989) that minimises the median number of patients required under the
null hypothesis, as opposed to the expected number of patients. A program is noted
as being available from the authors that performs the design search. The design differs
very little to that of Simon, other than when the response rate of the treatment is much
less than the null hypothesis rate. Termination at the end of the rst stage is for lack
of activity only.
DESIGNS WITH A SINGLE ARM 45
Shuster (2002)
Two-stage, binary outcome
Requires programming
Early termination for activity or lack of activity
The minimax design proposed by Shuster follows the same format as, for example,
Simon’s design (Simon 1989), although it allows early termination for activity at the
end of the rst stage, as well as for lack of activity. Sample sizes and cut-offs are
calculated based on exact type I and II errors, and the smallest expected maximum
sample size is calculated. The author shows that the proposed design generates the
smallest sample sizes under the null, alternative and maximum scenarios, compared
to Chang et al. (1987) and Fleming (1982). The author advises use of the proposed
minimax design when early termination for activity is benecial (giving as an example
the setting of paediatric cancer). A table of specic design scenarios is presented;
otherwise the design will require programming.
Tan and Machin (2002)
Two-stage, binary outcome
Standard software available
Early termination for lack of activity
Tan and Machin propose two Bayesian designs: the single threshold design (STD)
and the dual threshold design (DTD). The designs are intended to be user-friendly and
easily interpreted by those familiar with frequentist phase II designs. They provide
an alternative approach to the design, analysis and interpretation of phase II trial
data, allowing incorporation of relevant prior information and summarising results in
terms of the probability that a response proportion falls within a pre-specied region
of interest. The following design parameters are required: target response rate for
a new treatment; prior distribution for the experimental treatment being tested; the
minimum probability of the true response rate being at least the target response rate
at the end of stage 1 (for the STD, 𝜆1) and at the end of the study (𝜆2). For the DTD,
the lower response rate of no further interest is also required, and here 𝜆1 represents
the probability that the true response rate is lower than the rate of no further interest
at the end of stage 1.
The STD focuses on ensuring, at the end of the rst stage, that the nal response
rate of the drug has a reasonable probability of passing the target response rate at the
end of the trial. The DTD, however, focuses on ensuring, at the end of the rst stage,
that the nal response rate at the end of the trial is not below the response rate of no
further interest. Tables are given for a number of design scenarios and the designs
are compared with the frequentist approach of Simon (1989). Programs have been
developed and are available in Machin et al. (2008).
46 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Case and Morgan (2003)
Two-stage, binary outcome
Standard software available and programs noted as being available from authors
Early termination for lack of activity
Case and Morgan outline a design with survival outcomes which are dichotomised
to give survival probabilities at pre-specied time points of interest, incorporating all
available information. The design is aimed to avoid the drawbacks of extended follow-
up periods and breaks in recruitment during follow-up between stages. The design
does not require a halt in recruitment between stages as Nelson–Aalen estimates
of survival are used to incorporate all survival information up to the time point of
interest, at the time of interim analysis. Early termination is permitted only for lack
of activity. FORTRAN programs are noted as being available upon request from the
authors, to identify the optimal design, and the proposed design is also available in
Machin et al. (2008).
Jung et al. (2004)
Two-stage, binary outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Jung and colleagues propose a searching algorithm to identify admissible two-
stage designs based on Bayesian decision theory, incorporating a loss function which
is a weighted function of the expected number of patients and the maximum number
of patients required. A computer program, developed in Java and noted as being
available upon request from the authors, searches admissible designs (comparing the
expected loss to the Bayes risk) using information provided on the response rates
under null and alternative hypotheses, type I and II errors and maximum number
of patients available. Stopping rules are generated based on a minimum number of
responses required to be observed.
Lin and Shih (2004)
Two-stage, binary outcome
Programs noted as being available from authors
Early termination for lack of activity
Lin and Shih propose an adaptive design which allows sample size to be adjusted
at the end of the rst stage, to account for uncertainty in the response rate under the
alternative hypothesis. Two potential response rates are pre-specied at the design
stage, and the adjustment made based on these. Tables are provided and software is
DESIGNS WITH A SINGLE ARM 47
noted as being available from the authors to compute sample size and cut-offs that
are not displayed.
Wang et al. (2005)
Two-stage, binary outcome
Requires programming
Early termination for lack of activity
Wang and colleagues propose a Bayesian version of Simon’s two-stage design
(Simon 1989), controlling frequentist type I and II error rates, as well as Bayesian
error rates measured using posterior distributions. The design therefore allows incor-
poration of commonly controlled error rates familiar with frequentists, as well as
enabling calculation of posterior probabilities regarding treatment activity. Stopping
at the end of stage I is permitted for lack of activity only. Sample sizes and stopping
boundaries for each stage are provided in tables for specic design scenarios, and the
design is compared with that of Simon (1989) and the STD and DTDs of Tan and
Machin (2002). The design requires programming to enable implementation.
Banerjee and Tsiatis (2006)
Two-stage, binary outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Banerjee and Tsiatis propose an adaptive design that is similar to Simon’s optimal
design (Simon 1989); however, the sample size and decision criteria of the second
stage depend on the outcome of the rst stage, and the trial may terminate at the
end of the rst stage for either activity or lack of activity. The sample size and
decision criteria of the second stage are computed using Bayesian decision theory,
minimising the average sample size under the null hypothesis. The design offers
a small sample size reduction over Simon’s optimal design (3–5%); however, the
authors note potential difculties in planning a trial where the total sample size is
unknown at the outset. Tables are given for various design scenarios, and software is
noted as being available on request.
Ye and Shyr (2007)
Two-stage, binary outcome
Programs available on website
Early termination for lack of activity
The design proposed by Ye and Shyr follows that of Simon (1989) but is designed
to balance the number of patients investigated in each of the stages. Attention is
48 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
focused on a binary response outcome measure although the design may be extended
to multiple correlated outcome measures (where more than one outcome can occur
for one patient). Tables are provided with various design scenarios and software is
available at www.vicc.org/biostatistics/ts/freqapp.php (last accessed August 2013).
The authors note that when there are few patients available, Simon’s minimax
design would be preferable. If the optimal and minimax designs have dramatically
imbalanced sample sizes between the two stages then the proposed design may be
preferable; otherwise Simon’s optimal design can be used as this minimises the sam-
ple size under the null hypothesis. Termination at the end of the rst stage is for lack
of activity only.
Litwin et al. (2007)
Two-stage, binary outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Litwin and colleagues describe a design based on the outcome measure of PFS
at two set time points. In the second stage of the design the success rate of a binary
outcome measure at a set time point t2 is considered, which is dependent upon the
success rate of a, possibly different, binary outcome measure at an earlier set time
point t1 (assessed at the end of stage 1), for example, the progression-free rate at
time t2, dependent upon the progression-free rate at time t1. The design incorporates
the possibility of stopping for either activity or lack of activity at the end of the rst
stage and proceeds as follows:
1. n1 patients are recruited to the study and followed to time t1 for PFS.
2. If there are too few patients who are progression-free at time t1 then the trial
is stopped early for lack of activity.
3. If there are sufcient patients who are progression-free at time t1 then accrual
continues to the second stage until a total of n2 patients are recruited. Patients
in the initial cohort who are progression-free at t1 continue on in the study.
4. At the end of the second stage (t2) all n2–n1 patients from the second stage
and all those patients progression-free at t1 are evaluated at time t2.
Programs are noted as being available upon request from the authors.
Wu and Shih (2008)
Two-stage, binary outcome
Requires programming for adaptations
Early termination for lack of activity
DESIGNS WITH A SINGLE ARM 49
Wu and Shih propose approaches to handling data that deviate from the pre-
specied Simon’s two-stage design (Simon 1989). The following scenarios are
considered:
Simon’s design ‘interrupted’, such that there is additional evaluation at the
following times:
a. before completion of the rst stage;
b. after the rst stage but before completion of the second stage;
c. before completion of the rst stage and again before completion of the
second stage.
Simon’s design ‘abandoned’, that is, the rst unscheduled assessment leads to
abandoning the original design and an adapted assessment schedule is devel-
oped.
Adaptations to stopping rules are presented as well as detail regarding adjusting
the p-value associated with decision-making under the deviated scenario. Adaptations
are based on the conditional probability of passing the rst stage and the conditional
power of rejecting the null hypothesis assuming the study continues to its nal stage.
No software is detailed; however, sufcient detail is given to allow the design to be
programmed for implementation.
Koyama and Chen (2008)
Two-stage, binary outcome
Programs available on website
Early termination for lack of activity
Koyama and Chen detail an adaptation to Simon’s two-stage design (Simon
1989) to allow proper inference when the actual sample size at stage 2 deviates
from the planned sample size. The methodology allows computation of updated
critical values for the second stage, based on the number of responses observed
in the rst stage, and adapted p-values, point estimates and condence intervals,
incorporating conditional power. Software is available at http://biostat.mc.vanderbilt.
edu/wiki/Main/TwoStageInference (last accessed August 2013).
Chi and Chen (2008)
Two-stage, binary outcome
Standard programs available as per Simon’s two-stage design (Simon 1989)
Early termination for activity or lack of activity
Chi and Chen propose a curtailed sampling adaptation to Simon’s two-stage
design (Simon 1989). The design allows earlier termination of the trial in the event that
50 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
the treatment is either very active or very inactive. Detail of the proposed adaptations
is presented and is easily implemented, using standard software to identify a design
via Simon’s methodology (Simon 1989). The design can offer substantial savings in
sample sizes when compared to continuing recruitment to the predetermined number
of patients under Simon’s design.
Sambucini (2008)
Two-stage, binary outcome
Programs noted as being available
Early termination for lack of activity
Sambucini proposes a Bayesian design which represents a predictive version of
the STD proposed by Tan and Machin (2002), taking into account the uncertainty
about the data that have not yet been observed, to identify optimal two-stage sample
sizes and cut-off values. A ‘design’ prior and an ‘analysis’ prior are required to
be specied to compute prior predictive distributions and posterior probabilities of
treatment activity, respectively. A program written in R is available to determine
optimal two-stage designs.
3.2.2 Continuous outcome measure
Chang et al. (1999)
Two-stage, continuous outcome
Requires programming
Early termination for activity or lack of activity
Chang and colleagues outline a design for continuous or binary outcomes that
takes into account the number of patients on whom historical control data are based.
This reects the fact that the variances of the historical control data and the experi-
mental data will differ. The trial may be terminated at the end of the rst stage for
either activity or lack of activity. Algorithms are used to determine critical values for
stopping, and sample size is calculated by multiplying the single-stage sample size
(formulae provided) by between 1.02 and 1.05.
3.2.3 Multinomial outcome measure
Zee et al. (1999)
Two-stage, multinomial outcome
Requires programming
Early termination for activity or lack of activity
DESIGNS WITH A SINGLE ARM 51
Zee and colleagues propose single-stage and multi-stage single-arm designs con-
sidering a multinomial outcome, in the context of incorporating progressive disease
as well as response into the primary outcome measure. Analysis is based on the num-
ber of responses and progressions observed, compared with predetermined stopping
criteria. A computer program written in SAS identies the operating characteristics
of the designs. This is not noted as being available in the paper; however, detail is
given to allow implementation.
Lin and Chen (2000)
Two-stage, multinomial outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Lin and Chen detail a design that considers both CRs and PRs in a trinomial
outcome, weighting CR as the more desirable outcome. Investigators must specify
overall response rates under the null and alternative hypotheses, and the proportion
that is attributable to CR. A weighted score is calculated at the end of each stage
and this is compared with predetermined cut-off boundaries as in Simon’s optimal
and minimax designs (to which this paper may be viewed as an extension) (Simon
1989). Tables are given for specic scenarios; however, programs are noted as being
available upon request from the authors.
Panageas et al. (2002)
Two-stage, multinomial outcome
Programs noted as being available from authors
Early termination for lack of activity
Panageas and colleagues propose a single-arm two-stage design based on Simon’s
optimal design (Simon 1989), but with a trinomial outcome (e.g. CR vs. PR vs. non-
response). The design requires null and alternative response rates to be specied for
both CR and PR, that is, improvements in both categories are required. The optimal
design is identied iteratively, to minimise the expected sample size and to satisfy
the type I and II error rates. A computer program is noted as being available from
the authors, with specic design scenarios presented in tables. There is a marginal
saving on sample size over Simon’s design (Simon 1989). The design differs from
that of Zee et al. (1999), detailed above, since early termination is permitted for lack
of activity only and does not incorporate weighting of the different outcomes.
Lu et al. (2005)
Two-stage, multinomial outcome
Programs may be available from authors
Early termination for lack of activity
52 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Lu and colleagues propose a design (one-stage or two-stage) to look at both CR
and total response (or other such outcome measures whereby observing one outcome
implies the other outcome is also observed). The design recommends a treatment
for further investigation if either of the alternative hypotheses is met (i.e. for CR
or for total response) and rejects the treatment if neither is met. The designs follow
the general approach of Fleming’s single-stage (1982) or Simon’s two-stage (Simon
1989) approach whereby the number of CRs and total responses are compared to
identied stopping boundaries. Tables are provided for some combinations of null
and alternative hypotheses; however, formulae are given and at the time of manuscript
publication programs were in development. The design differs from others in this
section in that one outcome measure is a sub-outcome measure of the other, whereas
other designs consider discrete outcome measures such as PR versus CR.
Chang et al. (2007)
Two-stage, multinomial outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Chang and colleagues propose a single-stage and a two-stage design for win-
dow studies which aim to assess the potential activity of a new treatment in newly
diagnosed patients. Treatment is given to patients for a short period of time before
standard chemotherapy, and each patient is assessed for response or early progres-
sion (both binary outcome measures). The alternative hypothesis is based on both the
response rate being above a pre-specied rate and the early progressive disease rate
being below a pre-specied rate. The outcomes follow a multinomial distribution. A
SAS program is noted as being available from the authors to identify designs.
Gofn and Tu (2008)
Two-stage, multinomial outcome
Programs noted as being available from authors
Early termination for lack of activity
Gofn and Tu outline an adaptation to the design proposed by Zee et al. (1999),
based on a simulation approach to determine design. The authors note that the previous
design of Zee was found to have lower power than intended (Freidlin et al. 2002;
Zee et al. 1999). In the proposed two-stage design decision criteria are based on
the proportion of patients with response and the proportion of patients with early
progressive disease, in an advanced disease setting. The alternative hypothesis is that
the response rate is sufciently high or the early progressive disease rate is sufciently
low. Simulation is used to determine the required stopping boundaries to satisfy pre-
specied design criteria. Programs are noted as being available upon request from
the authors. Early termination is permitted for lack of activity only.
DESIGNS WITH A SINGLE ARM 53
Kocherginsky et al. (2009)
Two-stage, multinomial outcome
Programs available from website
Early termination for lack of activity
Kocherginsky and colleagues outline a design to consider the proportion of
patients achieving response and the proportion of patients not progressing early.
The alternative hypothesis being tested is that the response rate is sufciently high or
the non-progression rate is sufciently high. Sample size is calculated via numerical
searching, with the initial sample size estimate calculated following Simon’s two-
stage design (Simon 1989) based on the response rate limits. A numerical search is
then performed over all combinations of design parameters to determine stopping
rules, evaluated by assessing the probability of early termination and the probability
of rejecting the null hypothesis. The design incorporates a thorough assessment of
the operating characteristics over a range of response and progression rates, to guard
against unexpectedly high false-positive rates under certain parameters. Programs
written in R to implement the numerical search are noted as being available from
http://health.bsd.uchicago.edu/lestore/biostatlab/ (last accessed July 2013). Early
termination is permitted for lack of activity only.
Stallard and Cockey (2008)
Two-stage, multinomial outcome
Programs noted as being available from author
Early termination for lack of activity
Stallard and Cockey propose single-arm, one- and two-stage designs for ordered
categorical data, where the rejection region for the null hypothesis is dened based
on the likelihood ratio test. The null region over which the type I error is controlled
considers a weighting of the proportion of patients in each response category, in a
similar manner to that of Lin and Chen (2000). The focus of the paper is on response
with three levels; however, the design may be extended to more than three levels.
Programs are noted as being available from the rst author to allow identication of
designs.
3.2.4 Time-to-event outcome measure
Case and Morgan (2003)
Two-stage, time-to-event outcome
Standard software available and programs noted as being available from authors
Early termination for lack of activity
54 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Case and Morgan outline a design with survival outcomes which are dichotomised
to give survival probabilities at pre-specied time points of interest, incorporating all
available information. The design is aimed to avoid the drawbacks of extended follow-
up periods and breaks in recruitment during follow-up between stages. The design
does not require a halt in recruitment between stages as Nelson–Aalen estimates
of survival are used to incorporate all survival information up to the time point of
interest, at the time of interim analysis. Early termination is permitted only for lack
of activity. FORTRAN programs are noted as being available upon request from the
authors, to identify the optimal design, and the proposed design is also available in
Machin et al. (2008).
Litwin et al. (2007)
Two-stage, time-to-event outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Litwin and colleagues describe a design based on the outcome measure of
progression-free survival at two set time points, that is, a binary outcome. In the
second stage of the design the success rate of a binary outcome measure at a set
time point t2 is considered, which is dependent upon the success rate of a, possibly
different, binary outcome measure at an earlier set time point t1 (assessed at the end
of stage 1), for example, the progression-free rate at time t2, dependent upon the
progression-free rate at time t1. The design incorporates the possibility of stopping
for either activity or lack of activity at the end of the rst stage and proceeds as
follows:
1. n1 patients are recruited to the study and followed to time t1 for PFS.
2. If there are too few patients who are progression-free at time t1 then the trial
is stopped early for lack of activity.
3. If there are sufcient patients who are progression-free at time t1 then accrual
continues to the second stage until a total of n2 patients are recruited. Patients
in the initial cohort who are progression-free at t1 continue on the study.
4. At the end of the second stage (t2) all n2–n1 patients from the second stage
and all those patients progression-free at t1 are evaluated at time t2.
Programs are noted as being available upon request from the authors.
3.2.5 Ratio of times to progression
No references identied.
DESIGNS WITH A SINGLE ARM 55
3.3 Multi-stage designs
3.3.1 Binary outcome measure
Herson (1979)
Multi-stage, binary outcome
Programs noted as being available from author
Early termination for lack of activity
Herson describes a Bayesian multi-stage design that considers early stopping
rules based on the predictive probability that a treatment will not be successful at
the end of the phase II trial. Early termination is therefore only permitted for lack
of activity. The design incorporates investigators’ prior information on the response
rate of the experimental treatment and condence in this prior information (via a
coefcient of variation). Early termination boundaries are calculated based on pre-
specied sample sizes ranging from 20 to 30 patients, and consideration is also given
to the expected sample size of a subsequent phase III trial. Programs are noted as
being available from the author.
Fleming (1982)
Multi-stage, binary outcome
Standard software available for overall sample size
Early termination for activity or lack of activity
Fleming proposes a one-stage, two-stage and multi-stage design. The multi-stage
design addresses multiple testing considerations to allow early termination in the case
of extreme results, employing the standard single-stage test procedure at the last test.
Tables are presented for specic design scenarios using the exact underlying binomial
probabilities rather than the normal approximation to these probabilities. Programs
are readily available to calculate the overall sample size for a one-stage design (e.g.
Machin et al. 2008), with sample sizes at each stage chosen to be approximately
equal. Termination at the end of each stage is permitted for activity or lack of activity.
Bellissant et al. (1990)
Multi-stage, binary outcome
Requires programming
Early termination for activity or lack of activity
Bellissant and colleagues apply the triangular test (TT) and sequential probability
ratio test (SPRT), previously used in phase III trials, to single-arm group-sequential
56 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
phase II trials with a binary outcome. An efcient score, Z, and Fisher’s information,
V, are calculated derived from the likelihood function. The log odds ratio statis-
tic is used as the measure of the difference between the actual success rate and
the null hypothesis rate. Formulae are given for the calculation of Zand Vas well
as for calculation of the stopping boundaries, whereby Zis seen as the difference
between observed and expected number of responses under the null hypothesis and
Vas the variance of Zunder the null hypothesis. Early termination is permitted for
either activity or lack of activity. Sample size is justied via the operating charac-
teristics of the TT and SPRT, and group sizes and number of stages are arbitrary,
ranging from 5 to 15 in the examples. The design requires programming to enable
implementation.
Chen et al. (1994)
Multi-stage, binary outcome
Requires programming
Early termination for lack of activity
Chen and colleagues propose a multi-stage design that is an extension of Gehan’s
two-stage design (Gehan 1961), where the chance of stopping early is increased if
the observed response rate is smaller than that of interest. It is noted that this design
is suitable for phase II trials that have high expected response rates, in contrast to
the design of Gehan where the chance of stopping a trial early is low if the response
rate of interest is above 0.3. Limited tables of designs are presented, therefore addi-
tional designs will require programming. Early termination is permitted for lack of
activity only.
Ensign et al. (1994)
Multi-stage, binary outcome
Requires programming
Early termination for lack of activity
Ensign and colleagues propose a single-arm three-stage design that is an extension
to the two-stage design of Simon (1989). At the end of the rst stage, the trial is
terminated if no responses are observed (i.e. for lack of activity). If at least one
response is observed, stages 2 and 3 are carried out as per Simon’s stages 1 and 2.
The sample sizes and cut-offs for stages 2 and 3 are determined to minimise the
expected sample size under the null hypothesis. A restriction is made that the rst
stage must include at least ve patients. Extensive tables are provided for designs
under differing scenarios; however, the design will need programming to enable
implementation outwith those provided.
DESIGNS WITH A SINGLE ARM 57
Thall and Simon (1994a)
Multi-stage, binary outcome
Programs noted as being available from authors
Early termination for lack of activity
Thall and Simon present sample size calculations for their original Bayesian
continuous monitoring design (Thall and Simon 1994b). Adaptations to this design are
also provided. The impact of group-sequential monitoring, as opposed to continuous
monitoring, is assessed and it is found that assessment after every two, three or four
patients has little impact on results; however, reducing assessments much further can
increase the likelihood of inconclusive results. The rst adaptation considers early
stopping boundaries for inconclusive results. The second adaptation considers early
termination for lack of activity, which considers only lower stopping boundaries.
Software is noted as being available upon request to compute and implement each
of these designs, including the original continuous monitoring design (Thall and
Simon 1994b).
Tan and Xiong (1996)
Multi-stage, binary outcome
Programs available on website
Early termination for activity or lack of activity
Tan and Xiong propose a group-sequential (or continuous monitoring) design for
the assessment of a binary outcome in a single-arm trial, based on the sequential
conditional probability ratio test (SCPRT). The design is based around comparison to
a reference xed sample size test (RFSST) such as that proposed by Fleming (1982),
and the results that this would achieve, since it is desirable to preserve the power of
this test while incorporating additional opportunities to terminate the trial early. The
proposed design provides similar power to the xed sample size test, but allows more
opportunity to terminate the trial early (for activity or lack of activity). A FORTRAN
program is available via the website (http://lib.stat.cmu.edu/designs/scprtbin (last
accessed July 2013)) to compute the design characteristics.
Chen (1997)
Multi-stage, binary outcome
Program noted as being available from author
Early termination for lack of activity
Chen proposes an extension to Simon’s minimax and optimal two-stage designs
(Simon 1989), simply incorporating an additional stage. Tables are provided with
designs under various scenarios, and a FORTRAN program is noted as being available
58 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
from the author for other scenarios. When compared to Simon’s design, the three-stage
design sometimes has smaller expected sample size; however, this is not consistent.
Compared to Ensign’s three-stage design (Ensign et al. 1994), the proposed design
does not make restrictions on the size and cut-off for the rst stage.
Murray et al. (2004)
Multi-stage, binary outcome
Requires programming
Early termination for activity or lack of activity
Murray and colleagues detail calculation of stopping rules based on condence
interval estimation of the response rate at each stage. A table of specic design
scenarios is presented; however, the design requires programming to identify optimal
decision criteria for scenarios outwith the tables. The design is based on a pre-
specied xed sample size (i.e. no sample size calculation is performed) and a xed
number of stages (with xed sample size at each stage), with type I and II errors
evaluated for the resulting design. Early termination is permitted for either activity
or lack of activity. The design may be used when only a small number of patients
are available for study (30 patients considered in the motivating example) and exact
binomial calculations are employed.
Ayanlowo and Redden (2007)
Multi-stage, binary outcome
Requires programming
Early termination for lack of activity
Ayanlowo and Redden propose a stochastic curtailment design which is based
on the simple binomial test and considers the conditional probability of declaring a
treatment active at the end of the trial, conditional upon the responses observed to
date and the assumption that the alternative hypothesis is true. The design requires
programming to identify the points at which to conduct interim assessments. Sample
size determination is based on a binomial test. Stochastic curtailment adaptations to
Simon’s minimax and optimal design are also proposed (Simon 1989). While the
proposed designs provide more opportunity to stop a trial early due to an inactive
treatment, the authors suggest its use only when Simon’s minimax design is already
being considered, and when the trial is expected to recruit slowly and the outcome
may be observed relatively quickly.
Chen and Shan (2008)
Multi-stage, binary outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
DESIGNS WITH A SINGLE ARM 59
Chen and Shan outline a three-stage design, extending previous designs to allow
early termination for either activity or lack of activity (Chen 1997; Ensign et al.
1994; Simon 1989). Tables are given for optimal and minimax designs where the
difference in null and alternative hypothesis rates is 0.20 or 0.15, for a number of
scenarios. A C program is noted as being available from the authors to search for
designs under alternative scenarios. Comparing the proposed optimal and minimax
designs with those of Chen (1997), the designs presented in the current paper require
larger maximal sample size under the optimal design and similar maximal sample
size under the minimax design, but have a smaller average sample number in most
cases. Due to the ability to terminate early for either activity or lack of activity, the
probability of early termination at the rst stage and overall is higher for the current
designs compared to those of Chen (1997).
Lee and Liu (2008)
Multi-stage, binary outcome
Programs available from website
Early termination for lack of activity or activity
Lee and Liu outline a Bayesian group-sequential/continuous monitoring design
based on a binary outcome and the use of predictive probabilities (probability of a
positive result should the trial run to conclusion, given the interim data observed).
The design incorporates early termination for lack of activity, as well as activ-
ity. The continuous monitoring design is compared to Simon’s two-stage design
(Simon 1989). Under the proposed approach the probability of stopping the trial
early is higher, and in general, the expected sample size under the null hypothesis
is smaller. When assessing the design for robustness to deviation from continu-
ous monitoring, although the type I error rate is inated (usually less than 10%)
the design generally remains robust. The authors provide further considerations of
robustness to early termination, estimation bias and comparison to posterior probabil-
ity designs. Software is available from https://biostatistics.mdanderson.org/Software
Download/SingleSoftware.aspx?Software_Id=84 (last accessed July 2013) to allow
implementation.
3.3.2 Continuous outcome measure
No references identied.
3.3.3 Multinomial outcome measure
Zee et al. (1999)
Multi-stage, multinomial outcome
Requires programming
Early termination for activity or lack of activity
60 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Zee and colleagues propose single-stage and multi-stage single-arm designs con-
sidering a multinomial outcome, in the context of incorporating progressive disease
as well as response into the primary outcome measure. Analysis is based on the num-
ber of responses and progressions observed, compared with predetermined stopping
criteria. A computer program written in SAS identies the operating characteristics
of the designs. This is not noted as being available in the paper; however, detail is
given to allow implementation.
3.3.4 Time-to-event outcome measure
Cheung and Thall (2002)
Multi-stage, time-to-event outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Cheung and Thall propose a Bayesian sequential-adaptive procedure for contin-
uous monitoring, which may be extended to assessment after cohorts of more than
one patient, that is, multi-stage. The outcome measure of interest is a binary indica-
tor of a composite time-to-event outcome, utilising all the censored and uncensored
observations at each interim assessment. Continuous monitoring based on the approx-
imate posterior (CMAP) is used following Thall and Simon (1994b). The design can
incorporate multiple competing and non-competing outcomes. Early termination is
permitted for activity or lack of activity. R programs are noted as being available
from the authors to allow implementation of the design. This design enables data to
be incorporated on all patients at each interim assessment without all follow-up data
being obtained and may therefore be used when follow-up of each patient is for a
non-trivial period of time.
3.3.5 Ratio of times to progression
No references identied.
3.4 Continuous monitoring designs
3.4.1 Binary outcome measure
Thall and Simon (1994b)
Continuous monitoring, binary outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Thall and Simon propose a Bayesian continuous monitoring design to assess the
binary outcome of response in a single-arm trial. Information required includes prior
DESIGNS WITH A SINGLE ARM 61
information on the standard treatment, required improvement due to the experimental
treatment and minimum and maximum boundaries on sample size. A at prior is
assumed for the experimental treatment. Also required is a concentration parameter
for the experimental treatment, representing the amount of dispersion about the
mean of the experimental treatment. After the response outcome is observed on each
patient, the trial may be terminated for lack of activity, terminated for activity or
continued to the next patient (although this assessment is not required before the next
patient can be recruited). If the maximum sample size is obtained and neither of the
stopping boundaries for activity or lack of activity is crossed, the trial is declared
inconclusive. Stopping boundaries are calculated in terms of upper and lower posterior
probability limits, calculated by numerical integration. Designs should be assessed
by simulation to investigate the operating characteristics. Detail regarding software
and implementation is presented elsewhere (Thall and Simon 1994a).
Thall and Simon (1994a)
Continuous monitoring, binary outcome
Programs noted as being available from authors
Early termination for lack of activity
Thall and Simon present sample size calculations for their original Bayesian con-
tinuous monitoring design (Thall and Simon 1994b) outlined above. Adaptations to
this design are also provided. The rst adaptation considers early stopping boundaries
for inconclusive results. The second adaptation considers early termination for lack
of activity, which considers only lower stopping boundaries. Software is noted as
being available upon request to compute and implement the designs, including the
original continuous monitoring design (Thall and Simon 1994b).
Tan and Xiong (1996)
Continuous monitoring, binary outcome
Programs available on website
Early termination for activity or lack of activity
Tan and Xiong propose a group-sequential (or continuous monitoring) design for
the assessment of a binary outcome in a single-arm trial, based on the SCPRT.
The design is based around comparison to a RFSST such as that proposed by
Fleming (1982), and the results that this would achieve, since it is desirable to
preserve the power of this test while incorporating additional opportunities to ter-
minate the trial early. The proposed design provides similar power to the xed
sample size test, but allows more opportunity to terminate the trial early (for
activity or lack of activity). A FORTRAN program is available via the website
(http://lib.stat.cmu.edu/designs/scprtbin (last accessed July 2013)) to compute the
design characteristics.
62 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Chen and Chaloner (2006)
Continuous monitoring, binary outcome
Programs noted as being available from authors
Early termination for lack of activity
Chen and Chaloner propose a stopping rule for a Bayesian continuous monitoring
design. Stopping rules are based on both the posterior probability that the failure rate
is unacceptably high and the posterior probability that the failure rate is acceptably
low, where these high and low values are derived from historical data. Patients are
recruited until either the stopping rules are met or a maximum sample size has been
recruited. Programs are noted as being available in R (via contacting the authors) to
enable computation of the stopping boundaries and operating characteristics based
on maximum sample size available, prior information on the experimental treatment,
null and alternative hypothesis rates and the upper and lower posterior probability
bounds. Early termination is permitted only for lack of activity.
Lee and Liu (2008)
Continuous monitoring, binary outcome
Programs available from website
Early termination for lack of activity or activity
Lee and Liu outline a Bayesian group-sequential/continuous monitoring design
based on a binary outcome and the use of predictive probabilities (probability of a
positive result should the trial run to conclusion, given the interim data observed).
The design incorporates early termination for lack of activity, as well as activ-
ity. The continuous monitoring design is compared to Simon’s two-stage design
(Simon 1989). Under the proposed approach the probability of stopping the trial
early is higher, and in general, the expected sample size under the null hypothesis
is smaller. When assessing the design for robustness to deviation from continu-
ous monitoring, although the type I error rate is inated (usually less than 10%)
the design generally remains robust. The authors provide further considerations of
robustness to early termination, estimation bias and comparison to posterior probabil-
ity designs. Software is available from https://biostatistics.mdanderson.org/Software
Download/SingleSoftware.aspx?Software_Id=84 (last accessed July 2013) to allow
implementation.
Johnson and Cook (2009)
Continuous monitoring, binary outcome
Programs available on website
Early termination for lack of activity or activity
DESIGNS WITH A SINGLE ARM 63
Johnson and Cook propose a Bayesian continuous monitoring design based
on formal hypothesis tests. They argue that, in contrast to Bayesian designs based
on posterior credible intervals, any misspecication of prior densities associated
with the alternative hypothesis cannot bias the trial results in favour of the null
hypothesis when the proposed formal hypothesis test approach is used. Analysis
is performed after data are available for each patient, and the trial may be termi-
nated early for activity or lack of activity. Software is available from https://bio
statistics.mdanderson.org/SoftwareDownload/SingleSoftware.aspx?Software_Id=94
(last accessed July 2013) which allows the trial to be designed according to user-
specied priors.
3.4.2 Continuous outcome measure
No references identied.
3.4.3 Multinomial outcome measure
No references identied.
3.4.4 Time-to-event outcome measure
Cheung and Thall (2002)
Continuous monitoring, time-to-event outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
Cheung and Thall propose a Bayesian sequential-adaptive procedure for continu-
ous monitoring. The outcome measure of interest is a binary indicator of a composite
time-to-event outcome, utilising all the censored and uncensored observations at
each interim assessment. Continuous monitoring based on the approximate posterior
(CMAP) is used following Thall and Simon (1994b). The design can incorporate
multiple competing and non-competing outcomes. Early termination is permitted for
activity or lack of activity. R programs are noted as being available from the author
to allow implementation of the design. This design enables data to be incorporated
on all patients at each assessment without all follow-up data being obtained and may
therefore be used when follow-up of each patient is for a non-trivial period of time.
Thall et al. (2005)
Continuous monitoring, time-to-event outcome
Programs noted as being available from authors
Early termination for activity or lack of activity
64 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Thall and colleagues propose Bayesian continuous monitoring designs that incor-
porate three time-to-event outcomes (death, disease progression and SAE). Various
amendments to the design are proposed, including randomisation, frequent interval
monitoring, alternative distribution assumptions and incorporation of interval cen-
soring for disease progression. The trial may be stopped early for lack of activity or
for activity. Simulations are performed to establish operating characteristics of the
designs. Programs are noted as being available from the authors upon request.
Johnson and Cook (2009)
Continuous monitoring, time-to-event outcome
Programs available on website
Early termination for lack of activity or activity
Johnson and Cook propose a Bayesian continuous monitoring design based
on formal hypothesis tests. They argue that, in contrast to Bayesian designs based
on posterior credible intervals, any misspecication of prior densities associated
with the alternative hypothesis cannot bias the trial results in favour of the null
hypothesis when the proposed formal hypothesis test approach is used. Analysis
is performed after data are available for each patient, and the trial may be termi-
nated early for activity or lack of activity. Software is available from https://bio
statistics.mdanderson.org/SoftwareDownload/SingleSoftware.aspx?Software_Id=94
(last accessed July 2013) which allows the trial to be designed according to user-
specied priors.
3.4.5 Ratio of times to progression
No references identied.
3.5 Decision-theoretic designs
3.5.1 Binary outcome measure
Sylvester and Staquet (1980)
Decision-theoretic, binary outcome
Requires programming
Early termination for activity or lack of activity
Sylvester and Staquet outline a decision-theoretic design whereby the sample
size and cut-off boundaries for decision-making in the phase II trial are calculated
based on the number of patients who would be expected to receive the experimental
treatment in a subsequent phase III trial, as well as prior probabilities of the response
proportions of the experimental treatment in the phase II trial. There are examples
DESIGNS WITH A SINGLE ARM 65
of specic design scenarios; however, the design would require programming to
enable implementation. Decision criteria are based on observing a given number of
responses. The design allows incorporation of interim assessments, at which the trial
may be terminated early for either activity or lack of activity.
3.5.2 Continuous outcome measure
No references identied.
3.5.3 Multinomial outcome measure
No references identied.
3.5.4 Time-to-event outcome measure
No references identied.
3.5.5 Ratio of times to progression
No references identied.
3.6 Three-outcome designs
3.6.1 Binary outcome measure
Lee et al. (1979)
Three-outcome design, binary outcome
Requires programming
Early termination for activity or lack of activity
Lee and colleagues present a two-stage, three-outcome design whereby the avail-
able sample size is pre-specied based on non-statistical considerations such as
patient availability, and the optimal design is identied based on given constraints.
The design is based on determining whether the true response rate is above or below
a single pre-specied response rate, incorporating the possibility to declare an incon-
clusive result. Tables are presented for a target 20% response rate only, with upper
and lower limits of 30% and 10%, respectively, for determining activity, lack of
activity or an inconclusive result. The paper is therefore somewhat impractical for
designs beyond this specic setting, without further work to implement for other
scenarios. The design may be seen to complement the condence interval approach
to estimating a response rate with given precision.
66 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Storer (1992)
Three-outcome design, binary outcome
Programs noted as being available from author
Early termination for activity or lack of activity
Storer proposes a three-outcome design that is an adaptation to single-, two- and
multi-stage designs such as those described by Fleming (1982). The event rate of
uncertainty is taken to be around the midpoint between the event rate of no interest
and the event rate of interest. As described in Chapter 2, various error rates are
required to be specied. Here the probabilities of concluding in favour of either the
null or alternative hypothesis when in fact the true response rate lies within the region
of uncertainty are required to be specied. These error rates are set to be equal under
this design. Programs are noted as being available to identify the design, upon request
from the author. Early termination is permitted for activity or lack of activity in the
two- and multi-stage designs.
Sargent et al. (2001)
Three-outcome design, binary outcome
Requires programming; programs may be available upon request
Early termination for lack of activity
Sargent and colleagues propose a single-stage (and two-stage) design with three
possible outcomes. As described in Chapter 2, various error rates are required to be
specied, corresponding to differing regions of the distribution curves presented in
Figure 2.2. Here specic probabilities for concluding uncertainty are specied under
both the null and alternative hypotheses (𝜆and 𝛿, respectively, in Figure 2.2), and
these may differ. Tables and formulae are provided for sample size and stopping rule
calculation. The design requires programming; however, programs may be available
upon request from the authors. Under the two-stage design, early termination is for
lack of activity only.
3.6.2 Continuous outcome measure
No references identied.
3.6.3 Multinomial outcome measure
No references identied.
3.6.4 Time-to-event outcome measure
No references identied.
DESIGNS WITH A SINGLE ARM 67
3.6.5 Ratio of times to progression
No references identied.
3.7 Phase II/III designs
There are no phase II/III designs listed in this chapter since these designs require a
control arm to be incorporated in the phase II trial, to enable a seamless transition to
phase III.
4
Designs for single experimental
therapies including
randomisation
Sarah Brown
The designs included in this chapter incorporate randomisation to a control arm with
the intention of a formally powered statistical comparison between the experimental
and control arms, as well as designs where incorporation of randomisation is primarily
to provide a calibration arm, with no statistical comparison formally powered. The
distinction between these approaches is presented for each design listed.
4.1 One-stage designs
4.1.1 Binary outcome measure
Herson and Carter (1986)
One-stage, binary outcome
No formally powered statistical comparison between arms
Requires programming
Herson and Carter consider the inclusion of a randomised calibration group
in single-stage phase II trials of a binary endpoint, in order to reduce the risk of
A Practical Guide to Designing Phase II Trials in Oncology, First Edition.
Sarah R. Brown, Walter M. Gregory, Chris Twelves and Julia Brown.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 69
false-negative decision-making. Patients are randomised between current standard
treatment (calibration group) and the treatment under investigation. Results of the
calibration group are intended largely to assess the credibility of the outcome in the
experimental arm, that is, not for formal comparative purposes. Decision criteria are
based primarily on the experimental arm results; however, outcomes in the calibration
arm are also considered to address the initial assumptions made regarding the current
standard treatment. Thus the trial essentially constitutes two separate designs, one for
the experimental arm and one for the calibration arm. Due to the assessment of the
control arm results, the overall sample size of the trial may be between three and ve
times that of a non-calibrated design. An example is provided; however, the design
will require programming.
Thall and Simon (1990)
One-stage, binary outcome
No formally powered statistical comparison between arms
Requires programming
Thall and Simon outline a design that incorporates historical data, including
variability, into the design of the trial. A specic proportion of patients are randomised
to a control arm dependent upon the amount of historical control data available, the
degree of both inter-study and intra-study variability and the overall sample size of
the phase II study being planned (following formulae provided). The inclusion of a
sample of patients randomised to a control arm allows the precision of the response
rate in the experimental arm at the end of the trial to be maximised, relative to
the control. Sample size is determined iteratively and the design would need to be
programmed to allow implementation.
Stone et al. (2007b)
One-stage, binary outcome
Formally powered statistical comparison between arms
Standard software available
Stone et al. discuss the use of progressive disease rate at a given time point (as
well as overall progression-free survival) as an outcome measure in randomised phase
II trials of cytostatic agents. Formal comparison between the experimental treatment
and the control treatment is performed for superiority; however, larger type I error
rates than would be used in phase III are incorporated, and large treatment effects
are targeted. The use of relaxed type I errors and large targeted treatment effects
contribute to reduced sample sizes compared to phase III trials, and may therefore be
deemed more realistic for phase II trials.
70 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
4.1.2 Continuous outcome measure
Thall and Simon (1990)
One-stage, continuous outcome
No formally powered statistical comparison between arms
Requires programming
Thall and Simon outline a design that incorporates historical data, including
variability, into the design of the trial. A specic proportion of patients are randomised
to a control arm dependent upon the amount of historical control data available, the
degree of both inter-study and intra-study variability and the overall sample size of
the phase II study being planned (following formulae provided). The inclusion of a
sample of patients randomised to a control arm allows the precision of the outcome
estimate in the experimental arm at the end of the trial to be maximised, relative to
the control. Sample size is determined iteratively and the design would need to be
programmed to allow implementation.
Chen and Beckman (2009)
One-stage, continuous outcome
Formally powered statistical comparison between arms
Programming code provided
Chen and Beckman describe an approach to a randomised phase II trial design
that incorporates optimal error rates. Optimal type I and II errors for the design are
identied by means of an efciency score function which is based on initial proposed
error rates and the ratio of sample sizes between phases II and III. Sample size
calculation is performed using standard phase III-type approaches using the optimal
identied type I and II errors. Formal comparison with the control arm is incorporated.
The design considers cost efciency of the phase II and III trials, on the basis of the
ratio of sample sizes between phases II and III and the aprioriprobability of success
of the investigational treatment. An R program is provided in the appendix of the
manuscript to identify optimal designs.
4.1.3 Multinomial outcome measure
No references identied.
4.1.4 Time-to-event outcome measure
Simon et al. (2001)
One-stage, time-to-event outcome
Formally powered statistical comparison between arms
Standard software available
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 71
Simon and colleagues propose what is termed a randomised ‘phase 2.5’ trial
design, incorporating intermediate outcome measures such as progression-free sur-
vival. The design takes the approach of a phase III trial design, with a formally
powered statistical comparison with the control arm for superiority. It incorporates
a relaxed signicance level, large targeted treatment effects and intermediate out-
come measures, resulting in more pragmatic and feasible sample sizes than would be
required in a phase III trial. The design is straightforward, following the methodology
of phase III trials; however, it is important to note that this should only be used where
large treatment differences are realistic and should not be seen as a way to eliminate
phase III testing.
Stone et al. (2007b)
One-stage, time-to-event outcome
Formally powered statistical comparison between arms
Standard software available
Stone et al. discuss the use of progressive disease rate at a given time point, as
well as overall progression-free survival, as an outcome measure in randomised phase
II trials of cytostatic agents. Formal comparison between the experimental treatment
and the control treatment is performed for superiority; however, larger type I error
rates than would be used in phase III are incorporated, and large treatment effects
are targeted. The use of relaxed type I errors and large targeted treatment effects
contribute to reduced sample sizes compared to phase III trials, and may therefore be
deemed more realistic for phase II trials. This reects the designs described above
by Simon et al. in the setting of time-to-event outcomes, which are described by the
authors as ‘phase 2.5’ designs (Simon et al. 2001).
Chen and Beckman (2009)
One-stage, time-to-event outcome
Formally powered statistical comparison between arms
Programming code provided
Chen and Beckman describe an approach to a randomised phase II trial design
that incorporates optimal error rates. Optimal type I and II errors for the design are
identied by means of an efciency score function which is based on initial proposed
error rates and the ratio of sample sizes between phases II and III. Sample size
calculation is performed using standard phase III-type approaches using the optimal
identied type I and II errors. Formal comparison with the control arm is incorporated.
The design considers cost efciency of the phase II and III trials, on the basis of the
ratio of sample sizes between phases II and III and the aprioriprobability of success
of the investigational treatment. An R program is provided in the appendix of the
manuscript to identify optimal designs.
72 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
4.1.5 Ratio of times to progression
No references identied.
4.2 Two-stage designs
4.2.1 Binary outcome measure
Whitehead et al. (2009)
Two-stage, binary outcome
Formally powered statistical comparison between arms
Requires programming
Early termination for activity or lack of activity
Whitehead and colleagues outline a randomised controlled two-stage design with
normally distributed outcome measures that may be extended to the setting of binary
and ordinal outcomes. The design allows early termination for activity, or lack of
activity, and incorporates formal comparison between experimental and control arms.
At the interim assessment, which takes place after approximately half the total number
of patients have been recruited, sample size re-estimation may be incorporated if
necessary. The methodology employs approximations to the normal distribution since
sample sizes are generally large enough. No software is detailed as being available
to identify designs; however, programming is noted as being possible in SAS, and
detail is provided to allow its implementation. Simulation is also required to evaluate
potential designs.
Jung (2008)
Two-stage, binary outcome
Formally powered statistical comparison between arms
Programs noted as being available from author
Early termination for lack of activity
Jung proposes a randomised controlled extension to Simon’s optimal and minimax
designs (Simon 1989) in the context of a binary outcome measure (e.g. response). The
experimental arm is formally compared with the control arm and declared worthy of
further investigation only if there are sufciently more responders in the experimental
arm. Extensive tables are provided, and programs to identify designs not included in
tables are noted as being available upon request from the author. Extensions to the
design include unequal allocation, strict type I and II error control and randomisation
to more than one experimental arm.
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 73
Jung and George (2009)
Two-stage, binary outcome
Formally powered statistical comparison between arms
Requires minimal programming
Early termination for lack of efcacy
Jung and George propose methods of comparing treatment arms in a randomised
phase II trial, where the intention is either to determine whether a single treatment
is worthy of evaluation compared to a control or to select one treatment from many
for further evaluation. The phase II design for a single experimental treatment ver-
sus control is initially based on the evaluation of the control and experimental arms
independently following Simon’s two-stage design (Simon 1989), or similar. The
experimental treatment must rst be accepted via this evaluation, that is, compared
to historical control rates, and is then formally compared with the concurrent con-
trol arm. The experimental treatment is deemed worthy of further evaluation if the
treatment difference between the two arms is above some pre-dened value. No soft-
ware is detailed; however, detail is given which should allow implementation, and
sufcient examples are also provided. The initial two-stage design can be calculated
using standard software available for Simon’s two-stage design.
4.2.2 Continuous outcome measure
Whitehead et al. (2009)
Two-stage, continuous outcome
Formally powered statistical comparison between arms
Requires programming
Early termination for activity or lack of activity
Whitehead and colleagues outline a randomised controlled two-stage design with
normally distributed outcome measures. The design allows early termination for
activity, or lack of activity, and incorporates formal comparison between experimental
and control arms. At the interim assessment, which takes place after approximately
half the total number of patients have been recruited, sample size re-estimation may be
incorporated if necessary. The methodology employs approximations to the normal
distribution since sample sizes are generally large enough. No software is detailed as
being available to identify designs; however, programming is noted as being possible
in SAS, and detail is provided to allow its implementation. Simulation is also required
to evaluate potential designs. The authors note that the design may be extended to
binary and ordinal outcome measures.
74 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
4.2.3 Multinomial outcome measure
Whitehead et al. (2009)
Two-stage, multinomial outcome
Formally powered statistical comparison between arms
Requires programming
Early termination for activity or lack of activity
Whitehead and colleagues outline a randomised controlled two-stage design with
normally distributed outcome measures, which may be extended to binary and ordinal
outcome measures. The design allows early termination for activity, and lack of
activity, and incorporates formal comparison between experimental and control arms.
At the interim assessment, which takes place after approximately half the total number
of patients have been recruited, sample size re-estimation may be incorporated if
necessary. The methodology employs approximations to the normal distribution since
sample sizes are generally large enough. No software is detailed as being available
to identify designs; however, programming is noted as being possible in SAS, and
detail is provided to allow its implementation. Simulation is also required to evaluate
potential designs.
Sun et al. (2009)
Two-stage, multinomial outcome
Formally powered statistical comparison between arms
Software noted as being available from author
Early termination for lack of activity
Sun and colleagues propose a randomised two-stage design based on Zee’s single-
arm multi-stage design with multinomial outcome measure (Zee et al. 1999), adjusting
the rules such that a sufciently high response rate or a sufciently low early pro-
gressive disease rate should warrant further investigation of the treatment. Optimal
and minimax designs are proposed following the methodology of Simon (1989).
Differences in response and progressive disease rates between control and exper-
imental arms are compared, and the authors note that the intention of the phase
II trial is to screen for potential efcacy as opposed to identifying statistically
signicant differences. An extension is also proposed to the multi-arm selection
setting. Detail is given regarding how to implement the designs in practice, and
software is noted as being available by contacting the rst author to allow iden-
tication of designs. The design recommends a treatment for further investigation
when the response rate is sufciently high, or the early progressive disease rate is
sufciently low. Early termination is permitted for lack of activity only. The authors
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 75
also note that the design may be extended to studies monitoring safety and efcacy
simultaneously.
4.2.4 Time-to-event outcome measure
No references identied.
4.2.5 Ratio of times to progression
No references identied.
4.3 Multi-stage designs
4.3.1 Binary outcome measure
No references identied.
4.3.2 Continuous outcome measure
Cronin et al. (1999)
Multi-stage, continuous outcome
Formally powered statistical comparison between arms
Standard software available for sample size
Early termination for activity or lack of activity
Cronin and colleagues propose a Bayesian design for monitoring of phase II trials.
The design incorporates both sceptical and indifferent priors at each of the interim
analyses, according to the hypothesis being tested. Early termination is permitted for
activity or lack of activity, and as such, priors differ at interim and nal analysis.
Posterior distributions are updated at each analysis. When compared with frequentist
group-sequential methods, the proposed Bayesian methods performed at least as well
for the main purpose of detecting ineffective treatments early. The Bayesian method
was slowest to stop when the treatment had clear biological activity. The authors
note that the Bayesian method provides exibility to make changes to outcome
measures, analyses and original trial plans at interim analyses without introducing
theoretical statistical complications. Standard software is available for sample size
calculation.
4.3.3 Multinomial outcome measure
No references identied.
76 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
4.3.4 Time-to-event outcome measure
No references identied.
4.3.5 Ratio of times to progression
No references identied.
4.4 Continuous monitoring designs
4.4.1 Binary outcome measure
No references identied.
4.4.2 Continuous outcome measure
No references identied.
4.4.3 Multinomial outcome measure
No references identied.
4.4.4 Time-to-event outcome measure
Thall et al. (2005)
Continuous monitoring, time-to-event outcome
Formally powered statistical comparison between arms
Programs available from authors
Early termination for activity or lack of activity
Thall and colleagues propose Bayesian continuous monitoring designs that incor-
porate three time-to-event outcomes (death, disease progression and serious adverse
event). Various amendments to the initial proposed single-arm continuous moni-
toring design assuming exponential distribution are proposed (Cheung and Thall
2002), including randomisation, frequent interval monitoring, alternative distribution
assumptions and incorporation of interval censoring for disease progression. The trial
may be stopped early for lack of activity or for activity. Simulations are performed
to assess the performance of the design. Programs are noted as being available from
the authors upon request.
4.4.5 Ratio of times to progression
No references identied.
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 77
4.5 Three-outcome designs
4.5.1 Binary outcome measure
Hong and Wang (2007)
Three-outcome design, binary outcome measure
Formally powered statistical comparison between arms
Programs noted as being available from authors
Early termination for lack of activity
Hong and Wang detail both a single-stage and a two-stage three-outcome design
which extend that of Sargent et al. (2001) (Chapter 3) to a randomised comparative
design. The region of uncertainty falls around the middle region between the null
hypothesis that the difference in response rates between the arms is zero and the
alternative hypothesis that the difference is delta. In the two-stage design the trial
may only be terminated at the end of the rst stage for lack of activity. A SAS program
to identify the design is noted as being available on request from the authors.
4.5.2 Continuous outcome measure
No references identied.
4.5.3 Multinomial outcome measure
No references identied.
4.5.4 Time-to-event outcome measure
No references identied.
4.5.5 Ratio of times to progression
No references identied.
4.6 Phase II/III designs
4.6.1 Binary outcome measure
Storer (1990)
Phase II/III, binary outcome
No formal comparison with control in phase II
Standard software available for phase II, phase III requires programming
No early termination during phase II
78 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Storer proposes a phase II/III design with the same binary outcome at both stages.
This corresponds to a single-arm phase II design (e.g. A’Hern 2001) embedded in a
randomised phase III trial (i.e. randomisation takes place in phase II but the design
and primary decision-making are based on a single-arm design). The phase II decision
criteria are based on the results of the experimental arm only, as opposed to comparing
activity between the experimental and control arms. Sample size calculations for the
phase II aspect may be performed using standard available software for one-stage
designs, based on numerical searching to satisfy given type I and II errors and null
and alternative hypothesis response rates. Standard approaches to phase III sample
size calculation are used, with formulae provided to incorporate an adjustment for
the phase II/III design. This design may be used as a basis for phase II/III designs
whereby any single-arm phase II design is embedded in a phase III trial, including
where the outcome measure at phase III differs to that at phase II.
The design described above uses the same outcome measure at phase II as it does
at phase III. Although this may be seen as seamless phase II/III approach, in effect
it reects a phase III trial with an early interim analysis on the primary outcome
measure (albeit based on a single-arm design). In this setting, consideration should
be given to the most appropriate outcome measure to use for both the phase II and
phase III primary outcome. It is rare that efcacy in the phase III setting could be
claimed on the basis of a binary outcome; rather, a time-to-event outcome is usually
required in phase III trials.
Lachin and Younes (2007)
Phase II/III, binary outcome
Formally powered statistical comparison between arms
Requires programming
No early termination during phase II
Lachin and Younes outline a phase II/III design that incorporates different out-
come measures at phases II and III (with phase II being a shorter term outcome
measure). Joint distributions for the phase II and III outcomes are calculated, and
the design operating characteristics and sample sizes are calculated via iteration and
numerical integration. An estimate of the correlation between the two outcome mea-
sures is required. The design preserves the type I and II error rates, and patients
randomised during phase II are included in the phase III analysis. Analysis of the
phase II outcome measure considers a formal comparison for lack of activity only (or
excessive toxicity). Software is not detailed as being available; therefore, this design
would require programming to allow implementation.
Chow and Tu (2008)
Phase II/III, binary outcome
Formally powered statistical comparison between arms
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 79
Requires programming
No early termination during phase II
Chow and Tu present sample size formulae for seamless adaptive phase II/III
designs where the outcome measures at each phase differ, but the outcome measure
distributions remain the same (e.g. binary outcome in phase II, binary outcome in
phase III). This design is based on two separate studies, with differing endpoints and
durations, which are then combined. Data from patients in the phase II trial are used
to predict the phase III endpoint, for those patients, rather than continuing to follow
patients to observe the phase III endpoint. These data are then combined with the
data from the phase III trial. The relationship between the outcome measures at each
phase is required to be known and well established. This is an essential component
due to the predictive nature of the design. The design will require programming to
enable implementation.
4.6.2 Continuous outcome measure
Liu and Pledger (2005)
Phase II/III, continuous outcome
Formally powered statistical comparison between arms
Requires programming
No early termination during phase II
Liu and Pledger detail a phase II/III design for a single experimental treat-
ment compared to a control, as well as outlining a design in the dose-nding
context. In the single experimental treatment setting, the experimental treatment
is compared with the control treatment at the end of the phase II trial, based on
the short-term continuous outcome measure associated with the phase II trial. At
this stage, there is no break in recruitment during the analysis, and the sample
size for the phase III trial may be modied to allow estimation of the standard
deviation of the phase III outcome measure. Different phase II and III outcome
measures are used. At the end of the trial, the test statistics from the rst and sec-
ond stages (i.e. phases II and III) are combined. The treatment effect required to be
observed is the same for both short- and long-term outcome measures and needs to
be pre-specied, along with prior information on probability of success and stan-
dard deviation for each outcome measure. This information is used to generate the
operating characteristics of the design. Formulae are given which would need to
be implemented in order to identify the design. The design offers exibility in that
the second-stage (phase III) sample size may be calculated based on updated data
from the rst stage (phase II), and adaptation rules do not need to be specied
in advance.
80 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Lachin and Younes (2007)
Phase II/III, continuous outcome
Formally powered statistical comparison between arms
Requires programming
No early termination during phase II
Lachin and Younes outline a phase II/III design that incorporates different out-
come measures at phases II and III (with phase II being a shorter term outcome
measure). Joint distributions for the phase II and III outcomes are calculated, and
the design operating characteristics and sample sizes are calculated via iteration and
numerical integration. An estimate of the correlation between the two outcome mea-
sures is required. The design preserves the type I and II error rates, and patients
randomised during phase II are included in the phase III analysis. Analysis of the
phase II outcome measure considers a formal comparison for lack of activity only (or
excessive toxicity). Software is not detailed as being available; therefore, this design
would require programming to allow implementation. Detail is provided for binary
and continuous phase II outcome measures; however, extensions to time-to-event
outcomes are discussed.
Chow and Tu (2008)
Phase II/III, continuous outcome
Formally powered statistical comparison between arms
Requires programming
No early termination in phase II
Chow and Tu present sample size formulae for seamless adaptive phase II/III
designs where the outcome measures at each phase differ, but the outcome measure
distributions remain the same (e.g. binary outcome in phase II, binary outcome in
phase III). This design is based on two separate studies, with differing endpoints and
durations, which are then combined. Data from patients in the phase II trial are used
to predict the phase III endpoint, for those patients, rather than continuing to follow
patients to observe the phase III endpoint. These data are then combined with the
data from the phase III trial. The relationship between the outcome measures at each
phase is required to be known and well established. This is an essential component
due to the predictive nature of the design. The design will require programming to
enable implementation.
4.6.3 Multinomial outcome measure
No references identied.
RANDOMISED DESIGNS FOR SINGLE EXPERIMENTAL THERAPIES 81
4.6.4 Time-to-event outcome measure
Lachin and Younes (2007)
Phase II/III, time-to-event outcome
Formally powered statistical comparison between arms
Requires programming
No early termination during phase II
Lachin and Younes outline a phase II/III design that incorporates different out-
come measures at phases II and III (with phase II being a shorter term outcome
measure). Joint distributions for the phase II and III outcomes are calculated, and
the design operating characteristics and sample sizes are calculated via iteration and
numerical integration. An estimate of the correlation between the two outcome mea-
sures is required. The design preserves the type I and II error rates, and patients
randomised during phase II are included in the phase III analysis. Analysis of the
phase II outcome measure considers a formal comparison for lack of activity only (or
excessive toxicity). Software is not detailed as being available; therefore, this design
would require programming to allow implementation.
Chow and Tu (2008)
Phase II/III, time-to-event outcome
Formally powered statistical comparison between arms
Requires programming
No early termination in phase II
Chow and Tu present sample size formulae for seamless adaptive phase II/III
designs where the outcome measures at each phase differ, but the outcome measure
distributions remain the same (e.g. binary outcome in phase II, binary outcome in
phase III). This design is based on two separate studies, with differing endpoints and
durations, which are then combined. Data from patients in the phase II trial are used
to predict the phase III endpoint, for those patients, rather than continuing to follow
patients to observe the phase III endpoint. These data are then combined with the
data from the phase III trial. The relationship between the outcome measures at each
phase is required to be known and well established. This is an essential component
due to the predictive nature of the design. The design will require programming to
enable implementation.
4.6.5 Ratio of times to progression
No references identied.
82 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
4.7 Randomised discontinuation designs
4.7.1 Binary outcome measure
Kopec et al. (1993)
Randomised discontinuation, binary outcome
Formally powered statistical comparison between arms
Requires programming (can incorporate standard software)
No early termination
Kopec et al. introduce the randomised discontinuation design. All eligible patients
are initially treated with the investigational treatment for a pre-dened period of
time. At this time, all patients are assessed for response to treatment. Treatment
‘responders’ are randomised to either continue with the investigational treatment or
to discontinue the investigational treatment (and instead receive a placebo or current
standard treatment). A formal comparison is made between the experimental and
control arms at the end of the second stage (i.e. after randomisation). Formulae for
the calculation of response proportions are provided and are based on the sample
size needed for the randomised phase to assess relative activity. The design would
therefore need programming. Analysis may also be adapted to incorporate data from
patients in the rst stage, to adapt the response requirements for randomisation, for
example, to incorporate patients with stable disease or greater, as detailed by Rosner
et al. (2002). Alternatively, patients achieving response may continue with treatment,
those with progressive disease discontinue treatment and those with stable disease
are randomised (Stadler 2007). The current design, incorporating randomisation of
patients who are responding to treatment, may be more applicable to other disease
areas where life-threatening consequences of discontinuing treatment may be less
immediate, and there are fewer potential ethical implications associated with this.
4.7.2 Continuous outcome measure
No references identied.
4.7.3 Multinomial outcome measure
No references identied.
4.7.4 Time-to-event outcome measure
No references identied.
4.7.5 Ratio of times to progression
No references identied.
5
Treatment selection designs
Sarah Brown
The designs described within this chapter specically address the question of treat-
ment selection, that is, randomisation to multiple experimental treatment arms is
incorporated. It is, however, also possible to consider treatment selection using single-
arm or randomised phase II designs described in Chapters 3 and 4. In this respect the
aim is to show that each experimental treatment has sufcient activity (and tolerabil-
ity, if appropriate) before performing treatment selection. Treatment selection from
those experimental arms found to be sufciently active (and tolerable if appropriate)
may then take place, for example, using selection designs such as those described
by Sargent and Goldberg (2001) or Simon et al. (1985) (see Section 5.2.1 for further
details). These designs select the most active treatment with a pre-specied probabil-
ity of correct selection, according to the difference in activity observed between the
experimental arms. Such an approach, combining these selection designs with other
phase II designs, ensures that the treatments considered for selection have already
passed pre-specied minimum activity criteria (and possibly tolerability criteria),
prior to selection. Steinberg and Venzon provide an example of such an approach,
as described in Section 5.2.2 (Steinberg and Venzon 2002). The efciency of such
an approach, as compared with the alternative treatment selection designs described
within this chapter, should be considered in further detail on a trial-specic basis.
The designs within this chapter are organised as follows. First, designs including
a control arm are described in Section 5.1, organised by design category and by
outcome measure distribution. Second, in Section 5.2, designs that do not include
a control arm are presented, again by design category and by outcome measure.
Treatment selection designs that incorporate both activity and toxicity are presented
separately in Section 6.4.
A Practical Guide to Designing Phase II Trials in Oncology, First Edition.
Sarah R. Brown, Walter M. Gregory, Chris Twelves and Julia Brown.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
84 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
5.1 Including a control arm
5.1.1 One-stage designs
5.1.1.1 Binary outcome measure
No references identied.
5.1.1.2 Continuous outcome measure
No references identied.
5.1.1.3 Multinomial outcome measure
Whitehead and Jaki (2009)
One-stage, multinomial outcome, control arm
Formal comparison with control for selection
Programs noted as being available from authors
No early termination
Whitehead and Jaki propose one- and two-stage designs for phase II trials based
on ordered category outcomes, when the aim of the trial is to select a single treatment
to take forward to phase III evaluation. The design is randomised to incorporate a
formal comparison with a control arm, and hypothesis testing is based on the Mann–
Whitney statistic. The treatment identied with the smallest p-value indicating a
treatment effect is selected as the treatment to take forward for further investigation.
Details of sample size and critical value calculation are provided, and R code is noted
as being available from the authors to allow implementation. Specication of the
worthwhile treatment effect and the small positive treatment effect that is not worth
further investigation are required to be specied.
5.1.1.4 Time-to-event outcome measure
No references identied.
5.1.1.5 Ratio of times to progression
No references identied.
5.1.2 Two-stage designs
5.1.2.1 Binary outcome measure
Jung (2008)
Two-stage, binary outcome, control arm
Formal comparison with control for selection
TREATMENT SELECTION DESIGNS 85
Programs noted as being available from author
Early termination for lack of activity
Jung proposes a randomised controlled extension to Simon’s optimal and mini-
max designs (Simon 1989), considering a binary outcome measure and incorporating
early termination for lack of activity. The experimental arms are compared with
the control arm at the end of stage 1 and treatments may be dropped for lack of
activity. More than one experimental arm may therefore be taken forward to stage
2. If no treatments show improved activity over the control arm at the end of stage
1 the trial may be terminated for lack of activity. At the end of stage 2, all arms
that pass the stage 2 cut-off boundaries compared to control are deemed worthy of
further investigation. The selection design is an extension to the design described
comparing a single experimental arm with a control. In the selection design the
family-wise error rate, the probability of erroneously accepting an inactive treat-
ment, is controlled. Programs to identify designs are available upon request from
the author.
Jung and George (2009)
Two-stage, binary outcome, control arm
Formal comparison with control for selection
Requires minimal programming
Early termination for lack of activity
Jung and George propose methods of comparing treatment arms in a randomised
phase II trial, where the intention is either to select one treatment from many for
further evaluation or to determine whether a single treatment is worthy of evaluation
compared to a control. The phase II design is based on a k-armed trial (with or with-
out a control arm for selection) with each arm designed for independent evaluation
following Simon’s two-stage design (Simon 1989), or similar, based on historical con-
trol data, that is, no comparison is made with the control arm at this stage. Different
designs (i.e. the same two-stage design but with different operating characteristics)
may be used for different arms in the independent evaluation if deemed necessary.
A treatment must be accepted via the independent evaluation before it can be con-
sidered for selection, at which point comparisons may be made with the control
arm. p-Values are calculated to represent the probability that the difference between
the arms being compared is at least some pre-dened minimal accepted difference,
given the actual difference observed. The outcome measure used to select the better
treatment is the same outcome measure used for evaluation of each arm indepen-
dently, for example, tumour response. No software is detailed; however, detail is
given which should allow implementation, and sufcient examples are also provided.
The initial two-stage design can be calculated using software available for Simon’s
two-stage design.
86 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
5.1.2.2 Continuous outcome measure
Levy et al. (2006)
Two-stage, continuous outcome, control arm
No formal comparison with control for selection
Requires programming
No early termination; treatment selection at the end of stage 1
Levy et al. propose a randomised two-stage futility design incorporating treatment
selection at the end of the rst stage. At the end of the rst stage the ‘best’ treatment is
selected based on the treatment with the highest/lowest (‘best’) mean outcome, that is,
no comparison with control is made here. Sample size for the rst stage is calculated
to give at least 80% probability of correct selection. Patients then continue to be
randomised between control and the selected treatment, and data from the rst stage
is incorporated into the second-stage futility analysis, incorporating a bias correction.
The null hypothesis is that the selected treatment reduces the mean response by at
least x% compared to control; the alternative hypothesis is that the selected treatment
reduces the mean response by less than x% compared to control (reecting a futility
design). Sample size and power calculation details are provided in appendices.
Shun et al. (2008)
Two-stage, continuous outcome, control arm
No formal comparison with control for treatment selection
Requires programming
No early termination at the end of stage 1
Shun et al. propose a phase II/III or two-stage treatment selection design where
a single treatment is selected from two at the end of the rst stage. Randomisation
incorporates a control arm, with the intention of formal comparison at the end of the
second stage only, that is, no formal comparison for treatment selection. Treatment
selection is based on the experimental treatment with the highest/lowest (‘best’)
mean outcome. A normal approximation approach is proposed to avoid complex
numerical integration requirements. The design assumes that the treatment effects
of the experimental treatments are not the same. The practical approach to timing
of interim analysis addresses the need to perform this early in order to avoid type
I error ination and the need to perform this late enough such that there is a high
probability of correctly selecting the better treatment. No software is noted as being
available; however, detail is provided to allow implementation and a detailed example
is given. The authors note that this design can be extended to binary and time-to-
event outcome measures if the correlation between the nal and interim test statistics
is known.
TREATMENT SELECTION DESIGNS 87
5.1.2.3 Multinomial outcome measure
Sun et al. (2009)
Two-stage, multinomial outcome, control arm
Formal comparison with control
Software noted as being available from author
Early termination for lack of activity; early treatment selection
Sun and colleagues propose a randomised two-stage design based on Zee’s single-
arm multi-stage design with multinomial outcome measure (Zee et al. 1999), adjusting
the rules such that a sufciently high response rate or a sufciently low early pro-
gressive disease rate should warrant further investigation of a treatment. Optimal and
minimax designs are proposed following the methodology of Simon (1989), incor-
porating comparison with a control arm. Differences in response and progressive
disease rates between control and experimental arms are compared. The authors note
that the intention of the phase II trial is to screen for potential efcacy as opposed
to identifying statistically signicant differences compared with control. Patients are
randomised between multiple experimental treatments and a control arm. At the end
of the rst stage only those treatments that pass the stopping boundaries for both
response and progressive disease are continued to the second stage. If there is clear
evidence that one treatment is better than the other, selection may take place at the
end of the rst stage. If, at the end of the second stage, there is no clear evidence
that one experimental treatment is better than the other both arms may be considered
for further evaluation. Detail is given regarding how to implement the designs in
practice, and software is noted as being available by contacting the rst author to
allow identication of designs. The authors also note that the design may be extended
to studies monitoring safety and efcacy simultaneously.
Whitehead and Jaki (2009)
Two-stage, multinomial outcome, control arm
Formal comparison with control for selection
Programs noted as being available from authors
No early termination
Whitehead and Jaki propose one- and two-stage designs for phase II trials based
on ordered category outcomes, when the aim of the trial is to select a single treatment
to take forward to phase III evaluation. The design is randomised to incorporate a
formal comparison with a control arm, and hypothesis testing is based on the Mann–
Whitney statistic. In the two-stage design, treatment selection takes place at the end of
stage 1 whereby the treatment with the smallest p-value indicating a treatment effect is
selected as the treatment to take forward to stage 2. In stage 2, patients are randomised
between the selected treatment and control only. The nal analysis at the end of stage
88 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
2 is based on all data available on patients in the control arm and the selected treatment
arm. Details of sample size and critical value calculation are provided, and R code
is noted as being available from the authors to allow implementation. Specication
of the worthwhile treatment effect and the small positive treatment effect that is not
worth further investigation are required to be specied.
5.1.2.4 Time-to-event outcome measure
No references identied.
5.1.2.5 Ratio of times to progression
No references identied.
5.1.3 Multi-stage designs
5.1.3.1 Binary outcome measure
No references identied.
5.1.3.2 Continuous outcome measure
Cheung (2009)
Multi-stage, continuous outcome, control arm
Formal comparison with control for treatment selection
Requires programming
Early treatment selection and early termination for lack of activity
Cheung describes an adaptive multi-arm, multi-stage selection design incorpo-
rating a control arm and considering a normally distributed outcome measure. Two
multi-stage procedures are proposed: an extension of the sequential probability ratio
test (SPRT) with a maximum sample size and a truncated sequential elimination
procedure (ELIM). The SPRT method allows early selection of a treatment when
there is evidence of increased activity compared to control, whereas the ELIM pro-
cedure also allows early termination of arms for lack of activity. The proposed
procedures are compared with single-arm trials and the ELIM procedure is rec-
ommended over these, incorporating sample size reassessment at interim analyses.
Cohort sizes between interim assessments may range from 1 to 10 with little impact
on the design’s operating characteristics. Sample size formulae are provided which
will require implementing in order to identify the trial design.
5.1.3.3 Multinomial outcome measure
No references identied.
TREATMENT SELECTION DESIGNS 89
5.1.3.4 Time-to-event outcome measure
No references identied.
5.1.3.5 Ratio of times to progression
No references identied.
5.1.4 Continuous monitoring designs
No references identied.
5.1.5 Decision-theoretic designs
No references identied.
5.1.6 Three-outcome designs
No references identied.
5.1.7 Phase II/III designs – same primary outcome measure at
phase II and phase III
The designs outlined within this section incorporate the same primary outcome
measure for phase II assessment as that used for phase III. Although this may be
seen as a seamless phase II/III approach, in effect it reects a phase III trial with an
early interim analysis on the primary outcome measure. In this setting, consideration
should be given to the most appropriate outcome measure to use for both the phase II
and phase III primary outcome. It is rare that efcacy in the phase III setting could
be claimed on the basis of, for example, a binary outcome; rather, a time-to-event
outcome is usually required in phase III trials.
5.1.7.1 Binary outcome measure
Bauer et al. (1998)
Phase II/III, binary outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from authors
Early termination for efcacy at the end of phase II
Bauer and colleagues outline a simulation program for an adaptive two-stage
design with application to phase II/III and dose nding. Two outcomes may be con-
sidered, with one primary variable on which formal hypothesis testing is performed
and the other for which adaptations at the end of the rst stage may be based on.
The outcomes may be binary or continuous, or a combination. The same primary
outcome measure is used at each analysis. Simulation is required to identify the best
90 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
design according to various operating characteristics and the performance of different
designs. A program is detailed (the focus of the manuscript) to allow implementation,
which is noted as being available on request from the authors. At the end of the rst
stage the stage 1 hypothesis is tested, generating a p-value p1. At the end of the second
stage the stage 2 hypothesis is tested using only data obtained from patients in stage
2, generating a p-value p2. The overall hypothesis is then tested combining p1 and
p2 using Fisher’s combination test (Fisher 1932). Application is given to phase II/
III, with treatment selection at the end of stage 1: if the p-value is signicant that at
least one of the treatments is superior then the treatment with the ‘best’ outcome is
considered in phase III. The trial may also terminate early for efcacy at the end of
stage 1 if the p-value is signicant at the stage 2 signicance level.
Bauer and Kieser (1999)
Phase II/III, binary outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from author
Early termination for efcacy at the end of phase II
Bauer and Kieser detail a design that incorporates formal comparison of each of
the experimental arms with the control at the end of phase II (as well as testing whether
any of the treatments are superior to control). The same primary outcome measure is
used in both phases II and III. A xed sample size is used for phase II, however the
phase III sample size can be updated adaptively at the end of phase II. Stopping at the
end of phase II is permissible for either lack of efcacy or early evidence of efcacy.
The design also allows more than one treatment to be taken forward to phase III. At
the end of phase II the sample size may be re-estimated and the test statistics to use
at phase III are determined, according to the number of treatments taken forward and
the hypotheses to be tested. The decision criterion at the end of phase III is based on
Fisher’s combination test (Fisher 1932) whereby the p-values from both phases are
combined (as opposed to combining data from all patients). Simulation is required
as detailed in Bauer et al. (1998), as above. Examples are given in the dose-nding
setting and the authors note that the main advantage of this design is its exibility and
its control of the family-wise error rate. The design is similar to that detailed above
(Bauer et al. 1998) with the exception that the current paper gives more detail relating
to multiple comparisons between experimental treatments and control arm. When
considering either of these two designs, it is advised that both papers be considered
together since the software detailed in Bauer et al., above, is required to identify the
design proposed here.
Stallard and Todd (2003)
Phase II/III, binary outcome, control arm
Formal comparison with control for treatment selection
TREATMENT SELECTION DESIGNS 91
Programs noted as being available from authors
Early termination for efcacy at the end of phase II
Stallard and Todd propose a design whereby patients from phase II are incorpo-
rated in the phase III analysis, and treatment selection at the end of phase II is based
on the treatment with the largest test statistic using efcient scores and Fisher’s infor-
mation. A formal comparison is made between the selected treatment and control,
and the trial may be terminated early for lack of efcacy or superiority at this stage.
The type I error in the nal phase III analysis is adjusted for the treatment selection
in phase II. Overall sample size and phase II sample size are computed according to
group-sequential phase III designs such as those described by Whitehead (1997). A
computer program is noted as being available from the authors to calculate power for
stopping boundaries, according to pre-specied group sizes. The authors note that
the design is useful when one treatment is likely to be much better than the others at
phase II, as opposed to taking multiple treatments to phase III. Consideration should
also be given to the timing of the rst interim analysis (i.e. phase II assessment).
Too early and there is too little information, too late and there are too many patients
enrolled and thus potentially wasted resources.
Kelly et al. (2005)
Phase II/III, binary outcome, control arm
Formal comparison with control for treatment selection
Requires programming
Early termination for efcacy and lack of efcacy during phase II
Kelly and colleagues propose an adaptation to the design proposed by Stallard and
Todd (detailed above), such that more than one treatment may be selected at multiple
stages within the phase II part of the trial. Treatments are evaluated for selection
using Fisher’s information and an efcient score statistic which may be applied to
continuous, binary and failure time data. p-Values are calculated at each stage for
comparison of the best treatment with control. Only treatments within a pre-specied
margin of the efcient score statistic of the best treatment are continued to the next
stage, and all other treatments are dropped. Patients are randomised between control
and each of the treatments under investigation at each stage. The trial may stop for
efcacy or lack of efcacy at each stage. The example given is based on the use
of the triangular test described by Whitehead (1997), which uses expected Fisher’s
information to calculate operating characteristics.
Wang and Cui (2007)
Phase II/III, binary outcome, control arm
No formal comparison with control in phase II
92 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Requires programming
No early termination during phase II
Wang and Cui outline a design whereby patients are randomised to each of
the experimental treatments under investigation and a control arm, using response-
adaptive randomisation (the paper is written in the context of dose selection but
could be applied to treatment selection). The allocation ratios are calculated based on
distance conditional powers (i.e. the probability that the event rate for the treatment
under investigation is larger than some pre-specied xed rate, based on the observed
data and the fact that some patients will not yet have had their outcome observed).
The treatment to which most patients have been randomised is deemed the most
efcacious at the end of the recruitment period. This selected treatment is then
formally compared with the control treatment, forming the phase III comparison.
This design uses binary outcome measures such as treatment response, for both the
phase II treatment selection and the phase III formal comparison; although it is noted
that continuous outcomes may be used. Simulation is required to investigate the
design parameters, with sample size calculated based on the phase III comparison.
The design may be implemented with the development of programs based on formulae
provided.
5.1.7.2 Continuous outcome measure
Bretz et al. (2006)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Minimal programming required
Early termination for efcacy or lack of efcacy at the end of phase II
Bretz and colleagues outline a phase II/III design which allows treatment selection
at the interim assessment (i.e. at the end of phase II). The design allows data from
the rst stage to be incorporated into the nal analysis. Formal comparisons between
control and experimental treatments are performed at the end of each stage. Early
termination is permitted at the end of the rst stage (i.e. at the end of phase II) for
lack of efcacy or for early evidence of efcacy. Also at this time, if the study is to
be continued to phase III, adaptations to the design of the trial may be made such as
sample size reassessment based on the data observed to date. Final analysis includes
data from both stages, with decision criteria based on a combination of test results
(i.e. using methods such as Fisher’s product test of the conditional error function).
The closure principle is incorporated, such that a hypothesis is only rejected if it
and all associated intersection hypotheses are also rejected. Sample size formulae are
given to allow calculation. The design may be extended to multiple stages, in which
case early termination during the phase II aspect may be incorporated.
TREATMENT SELECTION DESIGNS 93
Bauer et al. (1998)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from authors
Early termination for efcacy at the end of phase II
Bauer and colleagues outline a simulation program for an adaptive two-stage
design with application to phase II/III and dose nding. Two outcomes may be con-
sidered, with one primary variable on which formal hypothesis testing is performed
and the other for which adaptations at the end of the rst stage may be based on.
The outcomes may be binary or continuous, or a combination. The same primary
outcome measure is used at each analysis. Simulation is required to identify the best
design according to various operating characteristics and the performance of different
designs. A program is detailed (the focus of the manuscript) to allow implementation,
which is noted as being available on request from the authors. At the end of the rst
stage the stage 1 hypothesis is tested, generating a p-value p1. At the end of the
second stage the stage 2 hypothesis is tested using only data obtained from patients in
stage 2, generating a p-value p2. The overall hypothesis is then tested combining p1
and p2 using Fisher’s combination test (Fisher 1932). Application is given to phase
II/III, with treatment selection at the end of stage 1: if the p-value is signicant that
at least one of the treatments is superior then the treatment with the ‘best’ outcome
is considered in phase III. The trial may also terminate early for efcacy at the end
of stage 1 if the p-value is signicant at the stage 2 signicance level.
Bauer and Kieser (1999)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from author
Early termination for efcacy at the end of phase II
Bauer and Kieser detail a design that incorporates formal comparison of each
of the experimental arms with the control at the end of phase II (as well as testing
whether any of the treatments are superior to control). The same primary outcome
measure is used in both phases II and III. A xed sample size is used for phase II,
however the phase III sample size can be updated adaptively at the end of phase
II. Stopping at the end of phase II is permissible for either lack of efcacy or early
evidence of efcacy and is based on p-value calculation. The design also allows more
than one treatment to be taken forward to phase III. At the end of phase II the sample
size may be re-estimated and the test statistics to use at phase III are determined,
according to the number of treatments taken forward and the hypotheses to be tested.
The decision criterion at the end of phase III is based on Fisher’s combination test
94 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
(Fisher 1932) whereby the p-values from both phases are combined (as opposed to
combining data from all patients). Simulation is required as detailed in Bauer et al.
(1998), as above. Examples are given in the dose-nding setting and the authors
note that the main advantage of this design is its exibility and its control of the
family-wise error rate. The design is similar to that detailed above (Bauer et al. 1998)
with the exception that the current paper gives more detail relating to the multiple
comparisons between experimental treatments and control arm. When considering
either of these two designs, it is advised that both papers be considered together since
the software detailed in Bauer et al., above, is required to identify the design proposed
here.
Stallard and Todd (2003)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from authors
Early termination for efcacy at the end of phase II
Stallard and Todd propose a design whereby patients from phase II are incorpo-
rated in the phase III analysis, and treatment selection at the end of phase II is based
on the treatment with the largest test statistic using efcient scores and Fisher’s infor-
mation. A formal comparison is made between the selected treatment and control,
and the trial may be terminated early for lack of efcacy or superiority at this stage.
The type I error in the nal phase III analysis is adjusted for the treatment selection
in phase II. Overall sample size and phase II sample size are computed according to
group-sequential phase III designs such as those described by Whitehead (1997). A
computer program is noted as being available from the authors to calculate power for
stopping boundaries, according to pre-specied group sizes. The authors note that
the design is useful when one treatment is likely to be much better than the others at
phase II, as opposed to taking multiple treatments to phase III. Consideration should
also be given to the timing of the rst interim analysis (i.e. phase II assessment).
Too early and there is too little information, too late and there are too many patients
enrolled and thus potentially wasted resources.
Kelly et al. (2005)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Requires programming
Early termination for efcacy and lack of efcacy during phase II
Kelly and colleagues propose an adaptation to the design proposed by Stallard and
Todd (detailed above), such that more than one treatment may be selected at multiple
stages within the phase II part of the trial. Treatments are evaluated for selection
TREATMENT SELECTION DESIGNS 95
using Fisher’s information and an efcient score statistic which may be applied to
continuous, binary and failure time data. p-Values are calculated at each stage for
comparison of the best treatment with control. Only treatments within a pre-specied
margin of the efcient score statistic of the best treatment are continued to the next
stage, and all other treatments are dropped. Patients are randomised between control
and each of the treatments under investigation at each stage. The trial may stop for
efcacy or lack of efcacy at each stage. The example given is based on the use
of the triangular test described by Whitehead (1997), which uses expected Fisher’s
information to calculate operating characteristics.
Wang (2006)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Requires programming
Early termination for efcacy at the end of phase II
Wang proposes an adaptive design with treatment selection at the end of phase
II. Patients are randomised between control and each of the experimental treatments
under investigation in phase II. The design controls the overall type I error and allows
the conditional error function of the phase III trial to depend on the data observed
during phase II. Maximum sample sizes are required to be specied and simulations
performed to evaluate expected sample size. The identication of the optimal design
requires detailed numerical integration. At the end of the rst stage the treatment with
the largest test statistic is selected to take forward to phase III; however, the trial could
also be stopped at this point (i.e. at the end of phase II) for efcacy or lack of efcacy.
There is formal comparison between each of the experimental arms and the control
arm at the end of phase II, and as long as at least one experimental treatment has
sufcient activity, a treatment is selected for further testing in phase III (or selected
as being superior if signicant enough). The design has been implemented in R and
formulae are given to allow this to be implemented in other software, therefore the
design would need programming.
Wang and Cui (2007)
Phase II/III, continuous outcome, control arm
No formal comparison with control in phase II
Requires programming
No early termination during phase II
Wang and Cui outline a design whereby patients are randomised to each of
the experimental treatments under investigation and a control arm, using response-
adaptive randomisation (the paper is written in the context of dose selection but
could be applied to treatment selection). The allocation ratios are calculated based
96 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
on distance conditional powers (i.e. the probability that the treatment effect for the
treatment under investigation is larger than some pre-specied xed value, based on
the observed data and the fact that some patients will not yet have had their outcome
observed). The treatment to which most patients have been randomised is deemed the
most efcacious at the end of the recruitment period. This selected treatment is then
formally compared with the control treatment, forming the phase III comparison.
This design as detailed uses binary outcome measures such as treatment response, for
both the phase II treatment selection and the phase III formal comparison, although it
is noted that continuous outcomes may be used. Simulation is required to investigate
the design parameters, with sample size calculated based on the phase III comparison.
The design may be implemented with the development of programs based on formulae
provided.
Shun et al. (2008)
Phase II/III, continuous outcome, control arm
No formal comparison with control for treatment selection
Requires programming
No early termination at the end of phase II
Shun et al. propose a phase II/III or two-stage treatment selection design where
a single treatment is selected from two at the end of the rst stage. Randomisation
incorporates a control arm, with the intention of formal comparison at the end of the
second stage only, that is, no formal comparison for treatment selection. Treatment
selection is based on the experimental treatment with the highest/lowest (‘best’)
mean outcome. A normal approximation approach is proposed to avoid complex
numerical integration requirements. The design assumes that the treatment effects of
the experimental treatments are not the same. The practical approach to timing of
interim analysis addresses the need to perform this early in order to avoid type I error
ination, and the need to perform this late enough such that there is a high probability
of correctly selecting the better treatment. No software is noted as being available;
however, detail is provided to allow implementation and a detailed example is given.
The authors note that this design can be extended to binary and time-to-event outcome
measures if the correlation between the nal and interim test statistics is known.
5.1.7.3 Multinomial outcome measure
Whitehead and Jaki (2009)
Phase II/III, multinomial outcome, control arm
Formal comparison with control for selection
Programs noted as being available from authors
No early termination during phase II
TREATMENT SELECTION DESIGNS 97
Whitehead and Jaki propose one- and two-stage designs for phase II trials based
on ordered category outcomes, when the aim of the trial is to select a single treatment
to take forward to phase III evaluation. The authors note that the two-stage design
detailed may be applied to the phase II/III setting, although renements to the design
may be required including the use of different outcome measures for treatment selec-
tion and nal analysis. The design is randomised to incorporate a formal comparison
with a control arm, and hypothesis testing is based on the Mann–Whitney statistic.
In the phase II/III setting, treatment selection takes place at the end of stage 1, that
is, phase II, whereby the treatment with the smallest p-value indicating a treatment
effect is selected as the treatment to take forward to stage 2, that is, phase III. Early
termination for lack of activity is permitted at the end of phase II. During phase III,
patients are randomised between the selected treatment and control only. The nal
analysis at the end of phase III is based on all data available on patients in the con-
trol arm and the selected treatment arm. Details of sample size and critical value
calculation are provided, and R code is noted as being available from the authors
to allow implementation. Specication of the worthwhile treatment effect and the
small positive treatment effect that is not worth further investigation are required to
be specied.
5.1.7.4 Time-to-event outcome measure
Bauer and Kieser (1999)
Phase II/III, time-to-event outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from author
Early termination for efcacy at the end of phase II
Bauer and Kieser detail a design that incorporates formal comparison of each
of the experimental arms with the control at the end of phase II (as well as testing
whether any of the treatments are superior to control). The same primary outcome
measure is used in both phases II and III. A xed sample size is used for phase II,
however the phase III sample size can be updated adaptively at the end of phase
II. Stopping at the end of phase II is permissible for either lack of efcacy or early
evidence of efcacy and is based on p-value calculation. The design also allows more
than one treatment to be taken forward to phase III. At the end of phase II the sample
size may be re-estimated and the test statistics to use at phase III are determined,
according to the number of treatments taken forward and the hypotheses to be tested.
The decision criterion at the end of phase III is based on Fisher’s combination test
(Fisher 1932) whereby the p-values from both phases are combined (as opposed to
combining data from all patients). Simulation is required as detailed in Bauer et al.
(1998). Examples are given in the dose-nding setting and the authors note that the
main advantage of this design is its exibility and its control of the family-wise error
rate. When considering this design, it is advised that the detail provided by Bauer et al.
98 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
(1998) also be reviewed since this paper outlines the software required to identify the
design proposed here.
Stallard and Todd (2003)
Phase II/III, time-to-event outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from authors
Early termination for efcacy at the end of phase II
Stallard and Todd propose a design whereby patients from phase II are incorpo-
rated in the phase III analysis, and treatment selection at the end of phase II is based
on the treatment with the largest test statistic using efcient scores and Fisher’s infor-
mation. A formal comparison is made between the selected treatment and control,
and the trial may be terminated early for lack of efcacy or superiority at this stage.
The type I error in the nal phase III analysis is adjusted for the treatment selection
in phase II. Overall sample size and phase II sample size are computed according to
group-sequential phase III designs such as those described by Whitehead (1997). A
computer program is noted as being available from the authors to calculate power for
stopping boundaries, according to pre-specied group sizes. The authors note that
the design is useful when one treatment is likely to be much better than the others at
phase II, as opposed to taking multiple treatments to phase III. Consideration should
also be given to the timing of the rst interim analysis (i.e. phase II assessment).
Too early and there is too little information, too late and there are too many patients
enrolled and thus potentially wasted resources.
Kelly et al. (2005)
Phase II/III, time-to-event outcome, control arm
Formal comparison with control for treatment selection
Requires programming
Early termination for efcacy and lack of efcacy during phase II
Kelly and colleagues propose an adaptation to the design proposed by Stallard and
Todd (detailed above), such that more than one treatment may be selected at multiple
stages within the phase II part of the trial. Treatments are evaluated for selection
using Fisher’s information and an efcient score statistic which may be applied to
continuous, binary and failure time data. p-Values are calculated at each stage for
comparison of the best treatment with control. Only treatments within a pre-specied
margin of the efcient score statistic of the best treatment are continued to the next
stage, and all other treatments are dropped. Patients are randomised between control
and each of the treatments under investigation at each stage. The trial may stop for
efcacy or lack of efcacy at each stage. The example given is based on the use
TREATMENT SELECTION DESIGNS 99
of the triangular test described by Whitehead (1997), which uses expected Fisher’s
information to calculate operating characteristics.
5.1.7.5 Ratio of times to progression
No references identied.
5.1.8 Phase II/III designs – different primary outcome
measures at phase II and phase III
The literature described within this section considers designs whereby different pri-
mary outcome measures are used for phase II and for phase III. Here the phase II
primary outcome measure should be selected based on the discussions provided in
Chapter 2, as this is not intended to be used for phase III decision-making.
5.1.8.1 Binary outcome measure
Todd and Stallard (2005)
Phase II/III, binary outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from the authors
No early termination during phase II
Todd and Stallard describe a design where treatment selection occurs at the
rst interim assessment (phase II) based on comparison of a short-term outcome
measure for each of the treatments versus control. Patients are randomised to
each of the experimental treatments and control during phase II, and then to the
selected treatment and the control during phase III. Phase III is carried out in a
group-sequential manner, with the experimental treatment compared to control in
terms of a longer term outcome measure. Selection at phase II is based on the
treatment with the largest test statistic, that is, there is formal comparison with
control but the study may only be terminated for lack of activity at this stage. The
trial protocol remains the same throughout the study; therefore, patients in phase II
can be incorporated in phase III. Required treatment effects (clinically signicant),
treatment effects that are still desirable but not clinically signicant and expected
correlation between phase II and III outcome measures are all required to identify
the complete phase II/III design. Formulae are given and programs are noted as
being available from the authors to calculate stopping boundaries.
5.1.8.2 Continuous outcome measure
Todd and Stallard (2005)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
100 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
Programs noted as being available from authors
No early termination during phase II
Todd and Stallard describe a design where treatment selection occurs at the
rst interim assessment (phase II) based on comparison of a short-term outcome
measure for each of the treatments versus control. Patients are randomised to each
of the experimental treatments and control during phase II, and then to the selected
treatment and the control during phase III. Phase III is carried out in a group-
sequential manner, with the experimental treatment compared to control in terms of
a longer term outcome measure. Selection at phase II is based on the treatment with
the largest test statistic, that is, there is formal comparison with control but the study
may only be terminated for lack of activity at this stage. The trial protocol remains
the same throughout the study; therefore, patients in phase II can be incorporated in
phase III. Required treatment effects (clinically signicant), treatment effects that are
still desirable but not clinically signicant and expected correlation between phase II
and III outcome measures are all required to identify the complete phase II/III design.
Formulae are given and programs are noted as being available from the authors to
calculate stopping boundaries.
Liu and Pledger (2005)
Phase II/III, continuous outcome, control arm
Formal comparison with control for treatment selection
Requires programming
No early termination during phase II
Liu and Pledger detail a phase II/III design in the dose-nding context where
patients are randomised to different doses and a placebo–control with the intention
of dose selection, as well as an adaptive two-stage phase II/III design where the
intention of phase II is to determine whether or not to continue to phase III, for a
single experimental treatment. Short-term continuous outcome measures are used at
the end of phase II to ‘prune’ the doses and to perform sample size adjustment for
the second stage (phase III), and long-term continuous outcome measures are used to
estimate the dose–response curve to calculate trend statistics for the analysis of the
phase III (and also at the end of phase II). Patients continue to be randomised to all
doses for a short period of phase III during the rst analysis for dose selection at the
end of phase II (i.e. there is no break in recruitment for phase II analysis), at which
point more than one dose may be carried forward. The treatment effect required to be
observed is the same for both short- and long-term outcome measures and needs to be
pre-specied, along with prior information on probability of success for each dose,
standard deviation for each outcome measure, the time period between enrolment of
the rst patient and the rst analysis and the likely recruitment in this period. This
information is used to generate the operating characteristics of the design. Formulae
are given which would need to be implemented in order to identify the design. The
TREATMENT SELECTION DESIGNS 101
design offers exibility in that the second stage (phase III) sample size may be
calculated based on updated data from the rst stage (phase II), and adaptation rules
do not need to be specied in advance.
Shun et al. (2008)
Phase II/III, continuous outcome, control arm
No formal comparison with control for treatment selection
Requires programming
No early termination at the end of phase II
Shun et al. propose a phase II/III or two-stage treatment selection design where
a single treatment is selected from two at the end of the rst stage (i.e. phase II).
Randomisation incorporates a control arm, with the intention of formal comparison at
the end of the second stage only, that is, no formal comparison for treatment selection.
Treatment selection is based on the experimental treatment with the highest/lowest
(‘best’) mean outcome. A normal approximation approach is proposed to avoid
complex numerical integration requirements. Where a different outcome measure is
used during phase II for treatment selection, the correlation between the phase II
and III outcome measures must be specied. The design assumes that the treatment
effects of the experimental treatments are not the same. The practical approach to
timing of interim analysis addresses the need to perform this early in order to avoid
type I error ination, and the need to perform this late enough such that there is a
high probability of correctly selecting the better treatment. No software is noted as
being available; however, detail is provided to allow implementation and a detailed
example is given. The authors note that this design can be extended to binary and
time-to-event outcome measures if the correlation between the nal and interim test
statistics is known.
5.1.8.3 Multinomial outcome measure
No references identied.
5.1.8.4 Time-to-event outcome measure
Royston et al. (2003)
Phase II/III, time-to-event outcome, control arm
Formal comparison with control for treatment selection
Some programming required before using standard software
No early termination during phase II
Royston and colleagues outline a multi-arm, two-stage design aimed at iden-
tifying treatments worthy of further consideration at the end of the rst stage by
102 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
comparing each treatment with a control arm using an intermediate outcome measure
of treatment activity. Only those treatments showing sufcient improvement in activ-
ity over control are continued to the second stage, at the end of which the treatments
are each compared with control using an outcome measure of primary interest (i.e.
different to that used at the end of the rst stage). Data from both stages of the trial
are incorporated in the nal analysis at the end of stage 2. The design may be seen
to reect a seamless phase II/III design with treatment selection at the end of phase
II, allowing more than one treatment to be continued into phase III. In evaluating
the operating characteristics of the design, an estimate of the correlation between the
treatment effects on the intermediate and nal outcome measures is required. The
authors propose an empirical approach to identifying this correlation using bootstrap
resampling of previous data sets, thus the design requires data of this type to be
available in order to allow implementation.
Todd and Stallard (2005)
Phase II/III, time-to-event outcome, control arm
Formal comparison with control for treatment selection
Programs noted as being available from authors
No early termination during phase II
Todd and Stallard describe a design where treatment selection occurs at the
rst interim assessment (phase II) based on comparison of a short-term outcome
measure for each of the treatments versus control. Patients are randomised to
each of the experimental treatments and control during phase II, and then to the
selected treatment and the control during phase III. Phase III is carried out in a
group-sequential manner, with the experimental treatment compared to control in
terms of a longer term outcome measure. Selection at phase II is based on the
treatment with the largest test statistic, that is, there is formal comparison with
control but the study may only be terminated for lack of activity at this stage. The
trial protocol remains the same throughout the study therefore patients in phase II
can be incorporated in phase III. Required treatment effects (clinically signicant),
treatment effects that are still desirable but not clinically signicant and expected
correlation between phase II and III outcome measures are all required to identify
the complete phase II/III design. Formulae are given and programs are noted as
being available from the authors to calculate stopping boundaries.
5.1.8.5 Ratio of times to progression
No references identied.
5.1.9 Randomised discontinuation designs
No references identied.
TREATMENT SELECTION DESIGNS 103
5.2 Not including a control arm
5.2.1 One-stage designs
5.2.1.1 Binary outcome measure
Whitehead (1985)
One-stage, binary outcome, no control arm
Requires programming
No early termination
Whitehead discusses a phase II selection design when there are a number of
treatments available for study, currently and expected in the near future, and a xed
number of patients available over a period of time. Patients are randomised to the
treatments currently available and new treatments may be entered as they become
available. A given number of patients are recruited to each treatment and analysis takes
place when all treatments have been considered. The design, for which no software
is detailed but for which formulae are given to allow implementation, identies the
optimal number of treatments (t) and patients per treatment (n) such that nt =total
number of patients available. The examples given consider trials including around
10 treatments, 6 patients per treatment, that is, 60 patients in total. Analysis takes
the form of an appropriate statistical model, tting treatment as a covariate and
incorporating other prognostic variables as necessary. The treatment with the largest
estimated benecial effect is then selected for further investigation in phase III. The
design allows modication such that more than one treatment may be taken forward
and such that cut-off boundaries may be incorporated to ensure a pre-specied level
of success. Any outcome measure distribution may be considered. No control patients
are incorporated and no assessment of risk of a false-negative result is considered.
It is noted that when only a few treatments are to be tested and when the number of
patients available is plentiful, this design may be less appropriate.
Simon et al. (1985)
One-stage, binary outcome, no control arm
Software available
No early termination
Simon et al. detail a selection procedure based on correctly selecting the treatment
with the higher event rate when the difference in event rates is at least d,somepre-
specied amount. The design proposed will always select a treatment, even if the
differences are <d, but will do so with less assurance that the correct treatment is
being selected. The design does not include a pre-specied minimum level of activity;
however, it may be applied as an addition to another trial design establishing minimum
levels of activity prior to treatment selection (as described in the introduction to this
104 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
chapter). The design is easily implemented in statistical programming software such
as SAS and is available in Machin et al (2008).
Sargent and Goldberg (2001)
One-stage, binary outcome, no control arm
Requires programming
No early termination
Sargent and Goldberg propose a similar treatment selection design to Simon
et al., described above. The treatment with the higher event rate is selected when
the difference between treatments in the event rate is at least d(required to be pre-
specied). If the difference is less than d, other criteria for selection can be used.
Sample size is selected by considering the probability that the better treatment is
correctly selected. Treatments do not have to pass given boundaries for minimum
activity, it is simply necessary for one treatment to be better than the other by at
least d. Sample size can be reduced by incorporating allowance to correctly pick the
better treatment when the result is ambiguous, that is, when the difference between
treatments is less than d, assume that, for example, 50% of the time the better
treatment would correctly be chosen based on other criteria. As described for the
design proposed by Simon et al., this design does not need to operate alone and
can be used in conjunction with other trial designs to ensure minimum levels of
activity and to generate sample size. The design is easily implemented in statistical
programming software such as SAS.
5.2.1.2 Continuous outcome measure
Whitehead (1985)
One-stage, continuous outcome, no control arm
Requires programming
No early termination
Whitehead discusses a phase II selection design when there are a number of
treatments available for study, currently and expected in the near future, and a xed
number of patients available over a period of time. Patients are randomised to the
treatments currently available and new treatments may be entered as they become
available. A given number of patients are recruited to each treatment and analysis takes
place when all treatments have been considered. The design, for which no software
is detailed but for which formulae are given to allow implementation, identies the
optimal number of treatments (t) and patients per treatment (n) such that nt =total
number of patients available. The examples given consider trials including around
10 treatments, 6 patients per treatment, that is, 60 patients in total. Analysis takes
the form of an appropriate statistical model, tting treatment as a covariate and
TREATMENT SELECTION DESIGNS 105
incorporating other prognostic variables as necessary. The treatment with the largest
estimated benecial effect is then selected for further investigation in phase III. The
design allows modication such that more than one treatment may be taken forward
and such that cut-off boundaries may be incorporated to ensure a pre-specied level
of success. Any outcome measure distribution may be considered. No control patients
are incorporated and no assessment of risk of a false-negative result is considered.
It is noted that when only a few treatments are to be tested and when the number of
patients available is plentiful, this design may be less appropriate.
5.2.1.3 Multinomial outcome measure
No references identied.
5.2.1.4 Time-to-event outcome measure
Whitehead (1985)
One-stage, time-to-event outcome, no control arm
Requires programming
No early termination
Whitehead discusses a phase II selection design when there are a number of
treatments available for study, currently and expected in the near future, and a xed
number of patients available over a period of time. Patients are randomised to the
treatments currently available and new treatments may be entered as they become
available. A given number of patients are recruited to each treatment and analysis takes
place when all treatments have been considered. The design, for which no software
is detailed but for which formulae are given to allow implementation, identies the
optimal number of treatments (t) and patients per treatment (n) such that nt =total
number of patients available. The examples given consider trials including around
10 treatments, 6 patients per treatment, that is, 60 patients in total. Analysis takes
the form of an appropriate statistical model, tting treatment as a covariate and
incorporating other prognostic variables as necessary. The treatment with the largest
estimated benecial effect is then selected for further investigation in phase III. The
design allows modication such that more than one treatment may be taken forward
and such that cut-off boundaries may be incorporated to ensure a pre-specied level
of success. Any outcome measure distribution may be considered. No control patients
are incorporated and no assessment of risk of a false-negative result is considered.
It is noted that when only a few treatments are to be tested and when the number of
patients available is plentiful, this design may be less appropriate.
5.2.1.5 Ratio of times to progression
No references identied.
106 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
5.2.2 Two-stage designs
5.2.2.1 Binary outcome measure
Weiss and Hokanson (1984)
Two-stage, binary outcome, no control arm
Standard software available
Early termination for lack of activity
Weiss and Hokanson discuss the concept of integrated phase II trials to minimise
the enrolment of an excessive number of patients into trials of potentially ineffective
drugs. It is assumed that a number of treatments are available for investigation at the
same time, which are ranked to determine which treatment should be assessed rst in a
sequence of integrated trials. The rst cohort of n1patients are recruited to treatment 1
and followed up for response, during which the next cohort of n1patients are recruited
to treatment 2, and so on. If a pre-specied number of responses are observed in any
of the treatment arms from the rst n1patients in that cohort, recruitment to further
treatment arms is halted and a further n2patients are recruited to the treatment on
which the responses were observed, essentially following an integrated scheme of
multiple trials based on Gehan’s design (Gehan 1961). The aim of the process is to
assess each treatment individually for its inclusion in a phase III trial, as opposed
to selecting one of the treatments alone to investigate further. As such, at the end
of the integrated process, a number of treatments may be deemed worthy of further
investigation.
Steinberg and Venzon (2002)
Two-stage, binary outcome, no control arm
Requires programming if design not in tables
Early treatment selection permitted
Steinberg and Venzon propose an early selection design which may be used in
conjunction with a single-arm phase II trial design (to generate sample size; the
authors use Simon’s two-stage design as an example). The proposed design incorpo-
rates assessment of two experimental treatments at the end of stage 1, selecting
the superior treatment as the treatment with an event rate at least x% higher
than the other treatment, with probability of correct selection z(xand zrequire
pre-specifying). Tables are given to identify the required difference in number of
events observed between the two arms to select the superior treatment early. If
no treatment is selected at the end of stage 1, the trial continues to randomise
between the two treatments with selection at the end, otherwise if a treatment is
selected, the trial continues recruiting patients to that arm to the desired number of
patients under the underlying design. When used in combination with, for example,
TREATMENT SELECTION DESIGNS 107
Simon’s two-stage design, each treatment arm must independently pass the stop-
ping boundaries at each stage to be evaluable for selection. This design may be
considered over single-stage selection designs to enable treatment selection as early
as possible.
Logan (2005)
Two-stage, binary outcome, no control arm
Requires programming if design not in tables
Early termination for lack of activity
Logan proposes a two-stage selection design based on an adaptation of Simon’s
two-stage design (Simon 1989), as applied to a randomised selection trial. Treatments
that do not pass the stopping criteria at the end of the rst stage are dropped, and
the sample size for the second stage is adapted based on the number of treatments
remaining for stage 2, up to a maximum sample size. Since the second-stage sample
size is adaptive, the cut-off boundaries for the second stage are also dependent on the
number of treatments continuing. The intention is that at the end of the second stage,
additional selection criteria may be applied to those treatments successfully passing
the nal stopping criteria, in the case of more than one treatment. The proposed design
offers a saving in the total number of patients as compared to Simon’s designs, when
it is anticipated that not all treatments will be highly active. Tables of sample sizes and
stopping boundaries are presented for various scenarios; however, additional designs
require computing.
Jung and George (2009)
Two-stage, binary outcome, no control arm
Requires minimal programming
Early termination for lack of activity
Jung and George propose methods of comparing treatment arms in a randomised
phase II trial, where the intention is either to select one treatment from many for
further evaluation or to determine whether a single treatment is worthy of evaluation
compared to a control. The phase II design is based on a k-armed trial (with
or without a control arm for selection) with each arm designed for independent
evaluation following Simon’s two-stage design (Simon 1989), or similar, based on
historical control data. Different designs (i.e. the same two-stage design but with
different operating characteristics) may be used for different arms in the independent
evaluation if deemed necessary. A treatment must be accepted via the independent
evaluation before it can be considered for selection, at which point between-arm
comparisons are made. p-Values are calculated to represent the probability that the
difference between the arms being compared is at least some pre-dened minimal
accepted difference, given the actual difference observed. The outcome measure
108 A PRACTICAL GUIDE TO DESIGNING PHASE II TRIALS IN ONCOLOGY
used to select the better treatment is the same outcome measure used for evaluation
of each arm independently, for example, tumour response. No software is detailed;
however, detail is given which should allow implementation, and sufcient examples
are also provided. The initial two-stage design can be calculated using software
available for Simon’s two-stage design.
5.2.2.2 Continuous outcome measure
No references identied.
5.2.2.3 Multinomial outcome measure
No references identied.
5.2.2.4 Time-to-event outcome measure
No references identied.
5.2.2.5 Ratio of times to progression
No references identied.
5.2.3 Multi-stage designs
5.2.3.1 Binary outcome measure
Thall et al. (2000)
Multi-stage, binary outcome, no control arm
Requires programming (simulation programs noted as being available from
author)
No early termination (multi-stage randomisation)
Thall et al. outline a design for treatment strategy selection that incorporates
response-adaptive randomisation within each patient, that is, future treatment strate-
gies for each patient depend on the treatments they have already received, and their
responses. This design is multi-stage in nature since patients are randomised at vari-
ous stages throughout their treatment schedule. Sample size requires investigation via
simulation, and the authors note that sample size should be determined empirically
rather than using simpler methods. Response is categorised as success or failure,
the criteria for which can be different for different stages of treatment. The ‘best’
treatment strategy is selected as that with the largest estimated success probability,
which can be assessed by considering responses to multiple treatment strategies for
each patient.
TREATMENT SELECTION DESIGNS 109
5.2.3.2 Continuous outcome measure
No references identied.
5.2.3.3 Multinomial outcome measure
No references identied.
5.2.3.4 Time-to-event outcome measure
Cheung and Thall (2002)
Multi-stage, time-to-event outcome, no control arm
Programs noted as being available from authors
Early termination for activity or lack of activity
Cheung and Thall propose a Bayesian sequential-adaptive procedure for contin-
uous monitoring, which may be extended to assessment after cohorts of more than
1 patient, that is, multi-stage. The outcome measure of interest is a binary indicator of
a composite time-to-event outcome, utilising all the censored and uncensored obser-