Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series, Vol. 38)

Bayesian Adaptive Methods for Clinical Trials (Chapman & Hall/CRC Biostatistics Series, Vol. 38)

<P>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, <STRONG>Bayesian Adaptive Methods for Clinical Trials</STRONG> explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis.</P> <P></P> <P>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.</P> <P></P> <P>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.</P> Scott Berry talks about the book on the CRC Press YouTube Channel.