Short description:

This book will present a unified and up-to date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The book will emphasize the practical implementation of these methods using standard statistical software such as R and STATA.

Long description:

This book presents a unified and up-to-date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The emphasis is on the conceptual underpinning of ROC analysis and the practical implementation in diverse scientific fields. A plethora of examples accompany the methodologic discussion using standard statistical software such as R and STATA. The book arrives after two decades of intensive growth in both the methods and the applications of ROC analysis and presents a new synthesis. The authors provide a contemporary, integrated exposition of ROC methodology for both classification and prediction and include material on multiple-class ROC. This book avoids lengthy technical exposition and provides code and datasets in each chapter. ROC Analysis for Classification and Prediction in Practice is intended for researchers and graduate students, but will also be useful for those that use ROC analysis in diverse disciplines such as diagnostic medicine, bioinformatics, medical physics, and perception psychology.

"This book fills a critical gap. I could not find another reference on the ROC curve as comprehensive as the one by Nakas, Bantis, and Gatsonis. This book should be recommended as an excellent reference textbook for anyone needing an in-depth understanding of the ROC curve or for a specialized graduate course." - Mauricio Tec, Journal of the American Statistical Association

Table of Contents:

1. Introduction  2. Measures of Diagnostic and Predictive Performance  3. Statistical inference for the ROC curve  4. Comparing ROC curves  5. The ROC surface and k-class classification for k > 2  6. ROC regression  7. Missing data and errors-in-variables in ROC analysis