Useful in many areas of medicine and biology, Bayesian methods are particularly attractive tools for the design of clinical trials and diagnostic tests, which are based on established information, usually from related previous studies. Advanced Bayesian Methods for Medical Test Accuracy begins with a review of the usual measures such as specificity, sensitivity, positive and negative predictive value, and the area under the ROC curve. Then the scope expands to cover the more advanced topics of verification bias, diagnostic tests with imperfect gold standards, and those for which no gold standard is available.
Promoting accuracy and efficiency of clinical trials, tests, and the diagnostic process, this book:
- Enables the user to efficiently apply prior information via a WinBUGS package
- Presents many ideas for the first time and goes far beyond the two standard references
- Integrates reader agreement with different modalities—X-ray, CT Scanners, and more—to study their effect on medical test accuracy
- Provides practical chapter-end problems
Useful for graduate students and consulting statisticians working in the various areas of diagnostic medicine and study design, this practical resource introduces the fundamentals of programming and executing BUGS, giving readers the tools and experience to successfully analyze studies for medical test accuracy.
Table of Contents
Introduction. Medical Tests and Preliminary Information. Preview of the Book. Fundamentals of Diagnostic Accuracy
Regression and Medical Test Accuracy. Agreement and Test Accuracy. Estimating Test Accuracy with an Imperfect Reference Standard. Verification Bias and Test Accuracy. Test Accuracy and Medical Practice. Accuracy of Combined Tests. Bayesian Methods for Meta-Analysis. Appendix: Introduction to WinBUGS.
Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books are Bayesian Analysis of Linear Models, Econometrics and Structural Change(written with Hiraki Tsurumi), Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.
"… very thorough coverage of topics that are commonly encountered in practice as well as numerous examples. This book is particularly appropriate for graduate students in statistics who have interests in diagnostic medicine. It is also valuable to consulting statisticians who have some basic knowledge of Bayesian methods and need such a reference to solve practical problems. … a great introduction to Bayesian methods specifically focused on solving medical test accuracy-related problems. It addresses popular topics in medical accuracy studies via Bayesian statistical methods to take advantage of prior information. Some complex problems, such as situations without a gold standard and partial verification bias, are also discussed. I recommend this book to graduate students in statistics or biostatistics and applied statisticians who are interested in medical diagnostic test accuracy."
—Xiaoye Ma, Journal of the American Statistical Association, December 2013
"Dr. Broemeling places all these considerations associated with test accuracy determinations into a pure Bayesian perspective. His book can be, in some sense, considered as a Bayesian counterpart to the books of Pepe and Zhou et al. The presentation is quite detailed and supported by a significant number of applications. … The literature review supporting the theoretical concepts about accuracy and the practical aspects associated with the applications is without a doubt extensive. … a good overview of the methods currently used to assess the accuracy of a medical test and their transposition to the Bayesian framework."
—Benoît Beck, CHANCE, August 2013
"It covers extensively most aspects of applied problems one can encounter when dealing with diagnostic accuracy studies. An attractive feature is that WinBUGS code needed for the implementation of the described methodologies is given in every chapter (also available on the author’s blog). A set of exercises is given at
click on http://medtestacc.blogspot.com