The Statistical Evaluation of Medical Tests for Classification and Prediction (Paperback)

This book describes statistical concepts and techniques for evaluating medical diagnostic tests and biomarkers for detecting disease. More generally, the techniques pertain to the statistical classification problem for predicting a dichotomous outcome. Measures for quantifying test accuracy are described including sensitivity, specificity, predictive values, diagnostic likelihood ratios and the Receiver Operating Characteristic Curve that is commonly used for continuous and ordinal valued tests. Statistical procedures are presented for estimating and comparing them. Regression frameworks for assessing factors that influence test accuracy and for comparing tests while adjusting for such factors are presented. This book presents many worked examples of real data and should be of interest to practicing statisticians or quantitative researchers involved in the development of tests for classification or prediction in medicine.

Margaret Sullivan Pepe is a Professor of Biostatistics, University of Washington; Fred Hutchinson Cancer Research Center, Washington, USA. Margaret Sullivan Pepe is a Professor of Biostatistics, University of Washington; Fred Hutchinson Cancer Research Center, Washington, USA.