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This book presents a unified and up-to date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. It emphasizes the practical implementation of these methods using standard statistical software such as R and STATA. Existing books tend to be specialized and/or focus on the theoretical derivations, with limited discussion of the use of the concepts and methods across diverse scientific fields and modest emphasis on the implementation of the methods.

ROC Analysis for Classification and Prediction in Practice

Introduction. Measures of Diagnostic and Predictive Accuracy. the Receiver Operating Characteristic (Roc) Curve. the Area Under the Roc Curve (Auc). Cutoff Point Selection Based on the Roc Curve. Comparisons of Roc Curves. Regression Models in Roc Analysis. Missing Data and Errors-in-Variables in Roc Analysis. Roc Analysis of Predictive Accuracy. the Roc Surface and Beyond. Special Topics in the Application of Roc Methods. Appendices.