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.
Table of Contents
Statistical Methods in Diagnostic Medicine
Preview of Book
Datasets for Book
Activities in Diagnostic Medicine
Accuracy and Agreement
Developmental Trials for Imaging
Protocol Review and Clinical Trials
OTHER DIAGNOSTIC PROCEDURES
Sentinel Lymph Node Biopsy for Melanoma
Tumor Depth for Diagnosis of Metastatic Melanoma
A Biopsy of Non-Small Cell Lung Cancer
Coronary Artery Disease
BAYESIAN METHODS FOR DIAGNOSTIC ACCURACY
Bayesian Methods for Diagnostic Accuracy: Binary and Ordinal Data
Bayesian Methods for Test Accuracy: Quantitative Variables
Comparing Accuracy between Modalities
Sample Size Determination
REGRESSION AND TEST ACCURACY
The Audiology Study
The ROC Curve and Patient Covariates
Agreement for Discrete Ratings
Agreement for a Continuous Response
Combining Reader Information
DIAGNOSTIC IMAGING AND CLINICAL TRIALS
Guidelines for Tumor Response
Bayesian Sequential Stopping Rules
Software for Clinical Trials
Imperfect Diagnostic Test Procedures
Test Accuracy and Survival Analysis
ROC Curves with a Non-Binary Gold Standard
Periodic Screening in Cancer
Decision Theory and Diagnostic Accuracy
It is interesting to read this book on Bayesian biostatistics and diagnostic medicine. … this book has several unique features. … an excellent introductory textbook on Bayesian methods and their application in diagnostic medicine. Non-experienced statisticians may also find that the systematic overview of the classification and purposes of the three phases in clinical trials and the basic Bayesian theory are useful references and would benefit from the program codes, particularly WinBUGS codes. …
—Pharmaceutical Statistics, 2011, 10
…the inclusion of plenty of real examples plus details of the necessary BUGS code was a very positive attribute. Some of the data sets are available for the reader to analyse and this would further enhance understanding. Overall, it is certainly a useful read or reference book for a practicing statistician with a good baseline theoretical knowledge who would like to expand their interest in this specific field of application.
—A. Wade, University College London, Journal of the Royal Statistical Society, Series A, 2010
This book is quite a good one for a statistician that is (or training to be) a statistical consultant to a cancer center department of diagnostic imaging … . If you are such a person, this book should be in your library.
—David Booth, Technometrics, August 2010
Drawing on his collaborative experiences with medical researchers and his long-standing interests in Bayesian methods, the author of this book shows how the Bayesian approach can be used to advantage when medical diagnosis is based on data with uncertainty. … a general strength of the book is careful discussion of study designs and protocols, which is a bonus relative to many biostatistical books written from a more narrow theory and methods perspective. … A real strength is the strong integration between models and concepts on the one hand, and real studies on the other hand. The inclusion of WinBUGS code is also a plus. … this book is highly recommended for anyone whose interests touch on the statistical side of diagnostic medicine.
—Biometrics, March 2009