Bayesian Adaptive Methods for Clinical Trials

Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer’s disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.

The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II–III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis.

For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites.
Scott Berry talks about the book on the CRC Press YouTube Channel.

Scott M. Berry is the President and Senior Statistical Scientist at Berry Consultants, a statistical consulting group specializing in adaptive clinical trial design in pharmaceutical and medical device research and development.
Bradley P. Carlin is Mayo Professor in Public Health and Head of the Division of Biostatistics at the University of Minnesota.
J. Jack Lee is Professor of Biostatistics at the University of Texas M.D. Anderson Cancer Center.
Peter Müller is a Robert R. Herring Distinguished Professor in Clinical Research in the Department of Biostatistics at the University of Texas M.D. Anderson Cancer Center.

Statistical Approaches for Clinical TrialsIntroduction Comparisons between Bayesian and frequentist approaches Adaptivity in clinical trials Features and use of the Bayesian adaptive approach

Basics of Bayesian InferenceIntroduction to Bayes’ theorem Bayesian inferenceBayesian computationHierarchical modeling and metaanalysis Principles of Bayesian clinical trial designAppendix: R Macros

Phase I StudiesRule-based designs for determining the MTDModel-based designs for determining the MTDEfficacy versus toxicityCombination therapyAppendix: R Macros
Phase II StudiesStandard designs Predictive probability Sequential stoppingAdaptive randomization and dose allocationDose ranging and optimal biologic dosing Hierarchical models for Phase II designs Decision theoretic designsCase studies: BATTLE and ISPY-2Appendix: R Macros

Phase III StudiesIntroduction to confirmatory studies Bayesian adaptive confirmatory trialsArm dropping Modeling and prediction Prior distributions and the paradigm clash Phase III cancer trials Phase II/III seamless trialsCase study: Ablation device to treat atrial fibrillation Appendix: R Macros

Special TopicsIncorporating historical data Equivalence studies MultiplicitySubgroup analysisAppendix: R Macros

References
Author Index
Subject Index