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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.

Bayesian Designs for Phase I-II Clinical Trials

It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical 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. Statistical Approaches for Clinical Trials Introduction Comparisons between Bayesian and frequentist approaches Adaptivity in clinical trials Features and use of the Bayesian adaptive approach.

Ablation device to treat atrial fibrillation Appendix: 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. The authors, while clearly advocating the use of Bayesian approaches, nevertheless take a very pragmatic approach to the issue.

1st Edition

They argue for Bayesian methods that demonstrate good frequentist properties and that are practical to use. This is a refreshing change from some other books and papers that also advocate Bayesian methods, but which while theoretically interesting, are difficult to implement in practice. In summary, I found this book to be well written and interesting.

Chapman & Hall/CRC Biostatistics Series

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. 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. We will send you an SMS containing a verification code. Please double check your mobile number and click on "Send Verification Code". Looking for beautiful books? Visit our Beautiful Books page and find lovely books for kids, photography lovers and more. Other books in this series. Inference Principles for Biostatisticians Ian C. Cluster Randomised Trials Richard J. 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: Ablation device to treat atrial fibrillation Appendix: The authors, while clearly advocating the use of Bayesian approaches, nevertheless take a very pragmatic approach to the issue.

Bayesian Adaptive Methods for Clinical Trials : Donald A. Berry :

They argue for Bayesian methods that demonstrate good frequentist properties and that are practical to use. This is a refreshing change from some other books and papers that also advocate Bayesian methods, but which while theoretically interesting, are difficult to implement in practice. In summary, I found this book to be well written and interesting.

It is very timely given the interest in adaptive designs, and should be a useful resource for statisticians and nonstatisticians alike interested in adaptive clinical trials from a Bayesian perspective. This is the right book to get if you are interested in Bayesian methods for adaptive designs. Chernick, Technometrics, August It is written in an extremely readable style and is furnished with numerous examples and a great deal of helpful supplementary code.

This work provides a good overall look at the Bayesian approach to clinical trials.