Bayesian Adaptive Methods for Clinical Trials

by Scott M. Berry; Bradley P. Carlin; J. Jack Lee; Peter Muller

• ISBN13: 9781439825518
• ISBN10: 1439825513
• Format: Nonspecific Binding
• Copyright: 2010-07-19
• Publisher: Taylor & Francis

Summary

Driven by modern computing capabilities, there has been a rapid increase of interest in Bayesian methods for the analysis of clinical trial data. This book provides an introduction to Bayesian adaptive methods for the design and analysis of clinical trials. It presents background material on Bayesian inference and covers clinical trials in Phases I, II, and III. The methods are illustrated with many detailed examples and case studies using real data obtained by the authors in their extensive work. The examples are implemented using R and WinBUGS software, which is made available on a supporting website.

Table of Contents

Statistical Approaches for Clinical Trials

Introduction

Comparisons between Bayesian and frequentist approaches

Adaptivity in clinical trials

Features and use of the Bayesian adaptive approach

Basics of Bayesian Inference

Introduction to Bayes' theorem

Bayesian inference

Bayesian computation

Hierarchical modeling and metaanalysis

Principles of Bayesian clinical trial design

Appendix: R Macros

Phase I Studies

Rule-based designs for determining the MTD

Model-based designs for determining the MTD

Efficacy versus toxicity

Combination therapy

Appendix: R Macros

Phase II Studies

Standard designs

Predictive probability

Sequential stopping

Adaptive randomization and dose allocation

Dose ranging and optimal biologic dosing

Hierarchical models for Phase II designs

Decision theoretic designs

Case studies: BATTLE and ISPY-2

Appendix: R Macros

Phase III Studies

Introduction to confirmatory studies

Bayesian adaptive confirmatory trials

Arm dropping

Modeling and prediction

Prior distributions and the paradigm clash

Phase III cancer trials

Phase II/III seamless trials

Case study: Ablation device to treat atrial fibrillation

Appendix: R Macros

Special Topics

Incorporating historical data

Equivalence studies

Multiplicity

Subgroup analysis

Appendix: R Macros

References

Author Index

Subject Index

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