Adaptive Design Theory and Implementation Using SAS and R (Chapman & Hall/CRC Biostatistics Series)

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This book provides a concise, unified presentation of adaptive design theories, uses SAS and R for the design and simulation of adaptive trials, and illustrates how to master different adaptive designs through real-world examples. It emphasizes the relationships among different methods; presents current regulatory views; discusses the challenges in planning, executing, analyzing, and reporting adaptive designs; and features Bayesian decision theory to optimize adaptive designs and programs. The author also explores controversial issues surrounding statistical theories as well as fruitful avenues for future research and applications of adaptive designs.

Adaptive Design Theory and Implementation Using SAS and R (Chapman & Hall/CRC Biostatistics Series) Review

This book just came out but I know a lot about it and about the author before I even got a copy. In November of last year Mark Chang coauthored a book in this Chapman and Hall series that I reviewed with praise because of the importance of the topic and the way it was demonstrated to work in a variety of real problems in pharmaceutical clinical trials. This book is even better as it goes more deeply into the methodology, the controversies and the results from simulation studies. Also it is much more practical because for every case where an application is given a SAS macro is also included to allow the reader to try the methodology for himself. In March of 2007 I actually designed a two-stage adaptive design with sample size reestimation for bioequivalence trials. I met mark at a conference where he presented much of his recent work and he was instrumental in helping me through his first book and his journal articles. This book had already gone to the publisher but he realized that this important design had overlooked. He added it when the copyedited version came to him. The design and the simulations related to it are very close to what I actually used. For those who like to program in R, he provides R code corresponding to each of the SAS macros that he gave. These programs make the new methodology readily available to interested users. The book is very comprehensive in that it covers a wide variety of applications for phase 2, phase 3 and combined phase trials. With the FDAs new initiative to speed up the drug discovery process this book will be an invaluable tool to statisticians in the pharmaceutical industry who would like to learn and apply these methods that along with the group sequential methodsare gaining favor within the FDA.

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