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Bayesian methods are being used more and more in medicine and biology. For example, at the University of Texas MD Anderson Cancer Center and other institutions, Bayesian sequential stopping rules are implemented somewhat routinely in the design of clinical trials. Also, Bayesian techniques are being used more frequently in diagnostic medicine, such as estimating the accuracy of diagnostic tests and for screening large populations for various diseases. Bayesian methods are quite attractive in many areas of medicine because they are based on prior information, which is usually available in the form of related previous studies. An example of this is in the planning of Phase II clinical trials, where a new therapy will be administered to patients who have advanced disease. Such therapies are developed by pharmaceutical companies and their success depends on the success of previous Phase I or other relevant Phase II trials. Bayes theorem allows a logical way to incorporate the previous information with the information that will accrue in the future. Accuracy of a medical test is an essential component of the diagnostic process and is the key issue of this book. Of course, medicine and biology are not the only areas where the concept of test accuracy plays a paramount role. For example, in the area of sports (e.g., cycling or baseball), the accuracy of a test for “doping” is of extreme importance for maintaining the integrity of the sport.
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