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Full Description
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 parametric ROC analysis, ROC methods for combined and pooled biomarkers, Bayesian hierarchical transformation models, sequential designs and inferences in the ROC setting, predictive modeling, multireader ROC analysis, and free-response ROC (FROC) methodology.
The book is suitable for graduate-level students and researchers in statistics, biostatistics, epidemiology, public health, biomedical engineering, radiology, medical imaging, biomedical informatics, and other closely related fields. Additionally, clinical researchers and practicing statisticians in academia, industry, and government could benefit from the presentation of such important and yet frequently overlooked topics.
Contents
Introduction: Background and Introduction. Methods for Univariate and Multivariate Data: Diagnostic Rating Scales. Monotone Transformation Models. Combination and Pooling of BiomarkersBayesian ROC Methods. Advanced Approaches and Applications: Sequential Designs of ROC Experiments. Multireader ROC Analysis. Free-Response ROC Analysis. Machine Learning and Predictive Modeling. Discussions and Extensions: Summary and Challenges. Section Appendices Symbols, Notations and Assumptions. Appendix A: Conventions. Appendix B: Notations. Appendix C: Abbreviations. Appendix D: Definitions and Terminologies. Appendix E: Remarks. Index.
