Front Cover; Contents; Authors; Preface; Chapter 1 -- Background and Introduction; Chapter 2 -- Diagnostic Rating Scales; Chapter 3 -- Monotone Transformation Models; Chapter 4 -- Combination and Pooling of Biomarkers; Chapter 5 -- Bayesian ROC Methods; Chapter 6 -- Sequential Designs of ROC Experiments; Chapter 7 -- Multireader ROC Analysis; Chapter 8 -- Free-Response ROC Analysis; Chapter 9 -- Machine Learning and Predictive Modeling; Chapter 10 -- Summary and Challenges; Appendix: Notation List; Back Cover
Summary
Statistical evaluation of diagnostic performance in general and Receiver Operating Characteristic (ROC) analysis in particular are important for assessing the performance of medical tests and statistical classifiers, as well as for evaluating predictive models or algorithms. This book presents innovative approaches in ROC analysis, which are relevant to a wide variety of applications, including medical imaging, cancer research, epidemiology, and bioinformatics. Statistical Evaluation of Diagnostic Performance: Topics in ROC Analysis covers areas including monotone-transformation techniques in
