There are numerous advantages to using Bayesian methods in
diagnostic medicine, which is why they are employed more and more
today in clinical studies. Exploring Bayesian statistics at an
introductory level, Bayesian Biostatistics and Diagnostic Medicine
illustrates how to apply these methods to solve important problems
in medicine and biology. After focusing on the wide range of areas
where diagnostic medicine is used, the book introduces Bayesian
statistics and the estimation of accuracy by sensitivity,
specificity, and positive and negative predictive values for
ordinal and continuous diagnostic measurements. The author then
discusses patient covariate information and the statistical methods
for estimating the agreement among observers. The book also
explains the protocol review process for cancer clinical trials,
how tumor responses are categorized, how to use WHO and RECIST
criteria, and how Bayesian sequential methods are employed to
monitor trials and estimate sample sizes. With many tables and
figures, this book enables readers to conduct a Bayesian analysis
for a large variety of interesting and practical biomedical
problems.
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