Statistical evaluation of diagnostic performance : topics in ROC analysis
- Boca Raton : CRC Press/Taylor & Francis, c2012.
- Physical description
- xviii, 227 p. : ill. ; 25 cm.
- Chapman & Hall/CRC biostatistics series.
RC71.35 .S73 2012
- Unknown RC71.35 .S73 2012
- Zou, Kelly H.
- Includes bibliographical references and index.
- Introduction Background and Introduction Background Information Gold Standard, Decision Threshold, Sensitivity, and Specificity Kappa Statistics Receiver Operating Characteristic Curve Area and Partial Area under ROC Curve Confidence Intervals, Regions, and Bands Point of Intersection and Youden Index Comparison of Two or More ROC Curves Approaches to ROC Analysis References Methods for Univariate and Multivariate Data Diagnostic Rating Scales Introduction Interpreter-Free Diagnostic Systems. Human Interpreter as Integral Part of Diagnostic System Remarks and Further Reading. References Monotone Transformation Models Introduction General Assumptions Empirical Methods Nonparametric Kernel Smoothing Parametric Models and Monotone Transformations to Binormal Distributions Confidence Intervals Concordance Measures in Presence of Monotone Transformations Intraclass Correlation Coefficient Remarks and Further Reading References Combination and Pooling of Biomarkers Introduction Combining Biomarkers to Improve Diagnostic Accuracy ROC Curve Analysis with Pooled Samples Remarks and Further Reading References Bayesian ROC Methods Introduction Methods for Sensitivity, Specificity, and Prevalence Clustered Data Structures and Hierarchical Methods Assumptions and Models for ROC Analysis Normality Transformation Elicitation of Prior Information Estimation of ROC Parameters and Characteristics Remarks and Further Reading References Advanced Approaches and Applications Sequential Designs of ROC Experiments Introduction Group Sequential Tests Using Large Sample Theory Sequential Evaluation of Single ROC Curve Sequential Comparison of Two ROC Curves Sequential Evaluation of Binary Outcomes Sample Size Estimation Remarks and Further Reading References Multireader ROC Analysis Introduction Overall ROC Curve and Its AUC Statistical Analysis of Cross-Correlated Multireader Data Remarks and Further Reading References Appendix 7.A: Closed Form Formulation of DBM Approach for Comparing Two Modalities Using Empirical AUC Appendix 7.B: Variance Estimators of Empirical AUCs Free-Response ROC Analysis Introduction FROC Approach Other Approaches of Detection-Localization Performance Assessment Remarks and Further Reading References Machine Learning and Predictive Modeling Introduction Predictive Modeling Cross-Validation Bootstrap Resampling Methods Overfitting and False Discovery Rate Remarks and Further Reading References Discussions and Extensions Summary and Challenges Summary and Discussion Future Directions in ROC Analysis Future Directions in Reliability Analysis Final Remarks Appendix: Notation List Index.
- (source: Nielsen Book Data)
- Publisher's 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 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.
(source: Nielsen Book Data)
- Publication date
- Kelly H. Zou ... [et al.].
- Chapman & Hall/CRC biostatistics series
- "A Chapman & Hall book."