Get Up to Speed on Many Types of Adaptive Designs
Since the publication of the first edition, there have been remarkable advances in the methodology and application of adaptive trials. Incorporating many of these new developments, Adaptive Design Theory and Implementation Using SAS and R, Second Edition offers a detailed framework to understand the use of various adaptive design methods in clinical trials.
New to the Second Edition
- Twelve new chapters covering blinded and semi-blinded sample size reestimation design, pick-the-winners design, biomarker-informed adaptive design, Bayesian designs, adaptive multiregional trial design, SAS and R for group sequential design, and much more
- More analytical methods for K-stage adaptive designs, multiple-endpoint adaptive design, survival modeling, and adaptive treatment switching
- New material on sequential parallel designs with rerandomization and the skeleton approach in adaptive dose-escalation trials
- Twenty new SAS macros and R functions
- Enhanced end-of-chapter problems that give readers hands-on practice addressing issues encountered in designing real-life adaptive trials
Covering even more adaptive designs, this book provides biostatisticians, clinical scientists, and regulatory reviewers with up-to-date details on this innovative area in pharmaceutical research and development. Practitioners will be able to improve the efficiency of their trial design, thereby reducing the time and cost of drug development.
Table of Contents
Theory of Hypothesis-Based Adaptive Design
Method with Direct Combination of P-values
Method with Inverse-Normal P-values
Adaptive Non-Inferiority Design With Paired Binary Data
Trial Design and Analysis with Incomplete Paired Data
Implementation of N-Stage Adaptive Designs
Conditional Error Function Method and Conditional Power
Recursive Adaptive Design
Unblinded Sample-Size Re-Estimation Design
Blinded Sample Size Re-Estimation
Adaptive Design with Co-Primary Endpoint
Multiple-Endpoint Adaptive Design
The Add-Arms Design For Unimodal Response
Biomarker-Informed Adaptive Design
Survival Modeling and Adaptive Treatment Switching
Response-Adaptive Allocation Design
Bayesian Adaptive Dose Finding Design
Bayesian Phase I-II Adaptive Design
Adaptive Design for Biosimilarity Trial
Multi-Regional Adaptive Trial Design
Bayesian Adaptive Design
Planning, Execution, Analysis, and Reporting
Data Analysis of Adaptive Design
Debates in Adaptive Designs
SAS Adaptive Design Modules: SEQDESIGN Procedure
Appendix A: Random Number Generation
Appendix B: A Useful Utility
Appendix C: SAS Macros for Add-Arm Designs
Appendix D: Implementing Adaptive Designs in R
"Rigidly adhering to frequentist principles can only take one so far. Mark Chang dedicates his marvellous tome to ‘those who are striving toward a better way’; this book is an ideal vessel with which to begin."
—International Society for Clinical Biostatistics, 2017
"... this text is a compilation of numerous adaptive trial designs that have been developed over the past few decades, including interim analyses, sample size re-estimation, biomarker enrichment, and response-adaptive treatment assignment. It is also an update to the first edition published in 2008 and includes 12 new chapters, as well as reformulations of several other chapters. ... Two strengths of this text are the numerous examples that are included throughout and the inclusion of open-ended problems at the end of chapters to help readers work through some of the concepts on their own. Another strength of this text is its attempt to cover as many adaptive designs as possible. ... This text is certainly a useful reference that covers approaches to adaptive clinical trial design, but would only be suitable for those with sufficient graduate-level statistical training and background in clinical trial design and programming."
—Thomas M. Braun, Department of Biostatistics, University of Michigan, writing in International Statistical Review, (2015), 83, 3, 511-522
Praise for the First Edition:
"The SAS and R programs are available on the web. This is good news since it avoids painful re-typing and error checking. … The book can provide all statisticians interested in the adaptive design field with an overview of the topic and software to run practical examples."
—Pharmaceutical Statistics, 2011, 10
"… this book covers many of the different forms of adaptive design. … The book also provides both SAS and R code to implement the theory, making the implementation of the theory much more accessible to readers. … Its strength is that it provides an overview on many different types of adaptive designs, with an excellent source of references. … a useful resource in this relatively new and quickly developing field."
—Patrick Kelly, Statistics in Medicine, Vol. 29, 2010
"This book provides a thorough overview of adaptive designs in clinical trials similar to another book on adaptive designs coauthored by Dr. Chang (Chow and Chang, 2006), but using a more theoretical framework. … this book is an excellent addition to the biostatistics book series … . It provides a comprehensive summary of adaptive designs that have been developed, and includes about 400 references in the bibliography. The general theory behind most of the adaptive designs is helpful in understanding their advantages over fixed designs, as well as their potential pitfalls. The SAS and R programs associated with each adaptive design make the book practical as well."
—Journal of the American Statistical Association, Vol. 104, No. 487, September 2009
"… this book provides a systematic introduction to adaptive design theory. It also gives trial examples and computing code to help readers construct a comprehensive understanding of adaptive designs. It is certainly a useful guide and reference for academic and industry statisticians alike."
—Feifang Hu, Biometrics, June 2009
"This easy-to-read book provides the reader with a unified and concise presentation of adaptive design theories, together with computer programs written in SAS and R for the design and simulation of adaptive trials. … The text, computer programs, and data sets will be of value to both practitioners and students alike."
—International Statistical Review, 2008