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Bayesian Biostatistics and Diagnostic Medicine

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Bayesian methods are being used more often than ever before in biology and medicine. For example, at the University of Texas MD Anderson Cancer Center, Bayesian sequential stopping rules routinely are used for the design of clinical trials. This book is based on the author’s experience working with a variety of researchers, including radiologists, pathologists, and medical oncologists. The majority of that experience has been with the Division of Diagnostic Imaging, where radiologists determine the extent of disease among patients undergoing treatment. Diagnosis, via medical imaging, is essential in order to assess the effect of the various therapies provided to the patient. Another source of information for the author has been the ability to work with medical oncologists in their design of Phase I, II, and III clinical trials. The author has found Bayesian methods for the design and analysis of clinical trials to be quite useful because prior information, in the form of previous related studies, is always available and easily incorporated into the design of future studies.

Based on this experience and the wealth of information available to the author, this book should give the biostatistics student a good idea of what to expect and how to work with healthcare researchers. It is an introductory book with a Bayesian flavor and is directed toward diagnostic medicine. Students with a good background in the basic methods courses of regression and the analysis of variance and in the introductory courses in probability and mathematical statistics should benefit greatly from the book. With this type of background, the student will be able to learn Bayesian statistics and how to apply it to important problems in medicine and biology. In addition, it should serve as a useful reference for those providing statistical assistance to medical scientists.

In the book, the reader is introduced to various diagnostic medical procedures, then presented with the fundamentals of Bayesian statistics and associated computing methods. Next, the foundation for the analysis of diagnostic test accuracy is outlined and the Bayesian way to analyze such data is explained, using many author-assisted studies. Of special interest is the estimation of the area under the receiver operating characteristic (ROC) curve for determining diagnostic accuracy. Also described in the book is a novel way to estimate the area when the image data are clustered.
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