Model-Assisted Bayesian Designs for Dose Finding and Optimization: Methods and Applications

234 Pages·2022·8.414 MB·English

by  Ying Yuan, Ruitao Lin, J. Jack Lee

Model-Assisted Bayesian
Designs for Dose Finding
and Optimization
Methods and Applications
Bayesian adaptive designs provide a critical approach to improve the efficiency and success of
drug development that has been embraced by the US Food and Drug Administration (FDA).
This is particularly important for early phase trials as they form the basis for the development
and success of subsequent phase II and III trials.
The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate
and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted
designs possess avant-garde features where superiority meets simplicity. Model-assisted
designs enjoy exceptional performance comparable to more complicated model-based adap-
tive designs, yet their decision rules often can be pre-tabulated and included in the protocol—
making implementation as simple as conventional algorithm-based designs. An example is
the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-
for-purpose designation from the FDA. This designation underscores the regulatory agency’s
support of the use of the novel adaptive design to improve drug development.
Features
• Represents the first book to provide comprehensive coverage of model-assisted
designs for various types of dose-finding and optimization clinical trials
• Describes the up-to-date theory and practice for model-assisted designs
• Presents many practical challenges, issues, and solutions arising from early-phase
clinical trials
• Illustrates with many real trial applications
• Offers numerous tips and guidance on designing dose finding and optimization trials
• Provides step-by-step illustrations of using software to design trials
• Develops a companion website (www.trialdesign.org) to provide freely available,
easy-to-use software to assist learning and implementing model-assisted designs
Written by internationally recognized research leaders who pioneered model-assisted designs
from the University of Texas MD Anderson Cancer Center, this book shows how model-
assisted designs can greatly improve the efficiency and simplify the design, conduct, and
optimization of early-phase dose-finding trials. It should therefore be a very useful practical
reference for biostatisticians, clinicians working in clinical trials, and drug regulatory profes-
sionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase
the new KISS principle: Keep it simple and smart!
Chapman & Hall/CRC Biostatistics Series
Series Editors
Shein-Chung Chow, Duke University School of Medicine, USA
Byron Jones, Novartis Pharma AG, Switzerland
Jen-pei Liu, National Taiwan University, Taiwan
Karl E. Peace, Georgia Southern University, USA
Bruce W. Turnbull, Cornell University, USA
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Model-Assisted Bayesian Designs for Dose Finding and Optimization
Methods and Applications
Ying Yuan, Ruitao Lin, and J. Jack Lee
For more information about this series, please visit: https://www.routledge.com/
Chapman--Hall-CRC-Biostatistics-Series/book-series/CHBIOSTATIS
Model-Assisted Bayesian
Designs for Dose Finding
and Optimization
Methods and Applications
Ying Yuan
The University of Texas MD Anderson Cancer Center, USA
Ruitao Lin
The University of Texas MD Anderson Cancer Center, USA
J. Jack Lee
The University of Texas MD Anderson Cancer Center, USA
First edition published 2023
by CRC Press
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and by CRC Press
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Library of Congress Cataloging-in-Publication Data
Names: Yuan, Ying (Professor of biostatistics), author. | Lin, Ruitao,
author. | Lee, J. Jack, author.
Title: Model-assisted Bayesian designs for dose finding and optimization :
methods and applications / Ying Yuan, Ruitao Lin, J. Jack Lee.
Other titles: Chapman & Hall/CRC biostatistics series.
Description: First edition. | Boca Raton : C&Hall/CRC Press, 2023. |
Series: Chapman & Hall/CRC biostatistics series | Includes
bibliographical references and index.
Identifiers: LCCN 2022017949 (print) | LCCN 2022017950 (ebook) | ISBN
9780367146245 (hardback) | ISBN 9781032357126 (paperback) | ISBN
9780429052781 (ebook)
Subjects: MESH: Drug Development--methods | Drug Design--methods | Drug
Dosage Calculations | Models, Statistical | Bayes Theorem | Clinical
Trials, Phase I as Topic
Classification: LCC RM301.25 (print) | LCC RM301.25 (ebook) | NLM QV 745
| DDC 615.1/9--dc23/eng/20220801
LC record available at https://lccn.loc.gov/2022017949
LC ebook record available at https://lccn.loc.gov/2022017950
ISBN: 978-0-367-14624-5 (hbk)
ISBN: 978-1-032-35712-6 (pbk)
ISBN: 978-0-429-05278-1 (ebk)
DOI: 10.1201/9780429052781
Typeset in CMR10
by KnowledgeWorks Global Ltd.
Publisher’s note: This book has been prepared from camera-ready copy provided by the authors.
To my wife, Suyu, and daughter, Selina.
Ying Yuan
To the memory of my mother, a brave cancer fighter.
Ruitao Lin
To my wife, Vei-Vei, and children, Joseph and Hope, for their
love and support.
J. Jack Lee

Contents
Preface xi
Author Biographies xiii
1 Bayesian Statistics and Adaptive Designs 1
1.1 Basics of Bayesian statistics . . . . . . . . . . . . . . . . . . 1
1.1.1 Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Bayesian inference . . . . . . . . . . . . . . . . . . . . 5
1.2 Bayesian adaptive designs . . . . . . . . . . . . . . . . . . . . 7
1.3 Adoption of Bayesian adaptive designs . . . . . . . . . . . . 10
2 Algorithm-Based and Model-Based Dose Finding Designs 13
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Traditional 3+3 design . . . . . . . . . . . . . . . . . . . . . 16
2.3 Cohort expansion . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Accelerated titration design . . . . . . . . . . . . . . . . . . . 20
2.5 Continual reassessment method . . . . . . . . . . . . . . . . 21
2.6 Bayesian model averaging CRM . . . . . . . . . . . . . . . . 25
2.7 Escalation with overdose control . . . . . . . . . . . . . . . . 28
2.8 Bayesian logistic regression method . . . . . . . . . . . . . . 30
2.9 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 Model-Assisted Dose Finding Designs 33
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.2 Modified toxicity probability interval design . . . . . . . . . 33
3.3 Keyboard design . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Bayesian optimal interval (BOIN) design . . . . . . . . . . . 38
3.4.1 Trial design . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4.2 Theoretical derivation . . . . . . . . . . . . . . . . . . 42
3.4.3 Specification of design parameters . . . . . . . . . . . 46
3.4.4 Statistical properties . . . . . . . . . . . . . . . . . . . 47
3.4.5 Frequently asked questions . . . . . . . . . . . . . . . 49
3.5 Operating characteristics . . . . . . . . . . . . . . . . . . . . 52
3.6 Software and case study . . . . . . . . . . . . . . . . . . . . . 57
vii
viii
4 Drug-Combination Trials 69
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 Model-based designs . . . . . . . . . . . . . . . . . . . . . . . 71
4.3 Model-assisted designs . . . . . . . . . . . . . . . . . . . . . . 74
4.3.1 BOIN combination design . . . . . . . . . . . . . . . . 74
4.3.2 Keyboard combination design . . . . . . . . . . . . . . 77
4.3.3 Waterfall design . . . . . . . . . . . . . . . . . . . . . 77
4.4 Operating characteristics . . . . . . . . . . . . . . . . . . . . 82
4.5 Software and case study . . . . . . . . . . . . . . . . . . . . . 85
5 Late-Onset Toxicity 93
5.1 A common logistical problem . . . . . . . . . . . . . . . . . . 93
5.2 Late-onset toxicities . . . . . . . . . . . . . . . . . . . . . . . 94
5.3 TITE-CRM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4 TITE-BOIN . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.4.1 Trial design . . . . . . . . . . . . . . . . . . . . . . . . 98
5.4.2 Incorporating prior information . . . . . . . . . . . . . 102
5.4.3 Statistical properties . . . . . . . . . . . . . . . . . . . 103
5.4.4 Operating characteristics . . . . . . . . . . . . . . . . 103
5.5 A unified approach using “effective” data . . . . . . . . . . . 105
5.6 TITE-keyboard and TITE-mTPI designs . . . . . . . . . . . 109
5.7 Software and case study . . . . . . . . . . . . . . . . . . . . . 112
6 Incorporating Historical Data 119
6.1 Historical data and prior information . . . . . . . . . . . . . 119
6.1.1 Incorporate prior information in CRM . . . . . . . . . 119
6.2 BOIN with Informative Prior (iBOIN) . . . . . . . . . . . . . 121
6.2.1 Trial design . . . . . . . . . . . . . . . . . . . . . . . . 121
6.2.2 Practical guidance . . . . . . . . . . . . . . . . . . . . 125
6.3 iKeyboard design . . . . . . . . . . . . . . . . . . . . . . . . 126
6.4 Operating characteristics . . . . . . . . . . . . . . . . . . . . 127
6.5 Software and case study . . . . . . . . . . . . . . . . . . . . . 128
7 Multiple Toxicity Grades 137
7.1 Multiple toxicity grades . . . . . . . . . . . . . . . . . . . . . 137
7.2 gBOIN accounting for toxicity grade . . . . . . . . . . . . . . 140
7.2.1 Trial design . . . . . . . . . . . . . . . . . . . . . . . . 140
7.2.2 Statistical derivation and properties . . . . . . . . . . 142
7.3 Multiple toxicity BOIN . . . . . . . . . . . . . . . . . . . . . 144
7.3.1 Trial design . . . . . . . . . . . . . . . . . . . . . . . . 144
7.3.2 Statistical derivation and properties . . . . . . . . . . 147
7.4 Software and illustration . . . . . . . . . . . . . . . . . . . . 149
ix
8 Finding Optimal Biological Dose 155
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
8.1.1 Phase I–II design paradigm . . . . . . . . . . . . . . . 156
8.2 EffTox design . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.2.1 Efficacy–toxicity trade-off . . . . . . . . . . . . . . . . 159
8.2.2 Joint probability model for efficacy and toxicity . . . . 160
8.2.3 Admissible rules . . . . . . . . . . . . . . . . . . . . . 161
8.2.4 Dose-finding algorithm . . . . . . . . . . . . . . . . . . 161
8.3 U-BOIN design . . . . . . . . . . . . . . . . . . . . . . . . . . 162
8.3.1 Utility-based risk-benefit trade-off . . . . . . . . . . . 163
8.3.2 Statistical model . . . . . . . . . . . . . . . . . . . . . 165
8.3.3 Admissible rules . . . . . . . . . . . . . . . . . . . . . 166
8.3.4 Dose-finding algorithm . . . . . . . . . . . . . . . . . . 167
8.3.5 Operating characteristics . . . . . . . . . . . . . . . . 170
8.4 BOIN12 design . . . . . . . . . . . . . . . . . . . . . . . . . . 171
8.4.1 Utility estimation using quasi-binomial likelihood . . . 173
8.4.2 Admissible rules and dose comparison . . . . . . . . . 174
8.4.3 Dose-finding rules . . . . . . . . . . . . . . . . . . . . 176
8.4.4 Operating characteristics . . . . . . . . . . . . . . . . 180
8.4.5 Extension to more complicated endpoints . . . . . . . 181
8.5 TITE-BOIN12 design . . . . . . . . . . . . . . . . . . . . . . 183
8.6 Other model-assisted phase I/II designs . . . . . . . . . . . . 188
8.6.1 uTPI design . . . . . . . . . . . . . . . . . . . . . . . . 188
8.6.2 STEIN and BOIN-ET designs . . . . . . . . . . . . . . 190
8.7 Software and case study . . . . . . . . . . . . . . . . . . . . . 193
Bibliography 205
Index 217