1st Edition

Statistical Evaluation of Diagnostic Performance Topics in ROC Analysis

    246 Pages 33 B/W Illustrations
    by Chapman & Hall

    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.

    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

    Methods for Univariate and Multivariate Data
    Diagnostic Rating Scales
    Interpreter-Free Diagnostic Systems.
    Human Interpreter as Integral Part of Diagnostic System
    Remarks and Further Reading.
    Monotone Transformation Models
    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
    Combination and Pooling of Biomarkers
    Combining Biomarkers to Improve Diagnostic Accuracy
    ROC Curve Analysis with Pooled Samples
    Remarks and Further Reading
    Bayesian ROC Methods
    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

    Advanced Approaches and Applications
    Sequential Designs of ROC Experiments

    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
    Multireader ROC Analysis
    Overall ROC Curve and Its AUC
    Statistical Analysis of Cross-Correlated Multireader Data
    Remarks and Further Reading
    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
    FROC Approach
    Other Approaches of Detection–Localization Performance Assessment Remarks and Further Reading References
    Machine Learning and Predictive Modeling
    Predictive Modeling
    Bootstrap Resampling Methods
    Overfitting and False Discovery Rate
    Remarks and Further Reading

    Discussions and Extensions

    Summary and Challenges

    Summary and Discussion
    Future Directions in ROC Analysis
    Future Directions in Reliability Analysis
    Final Remarks

    Appendix: Notation List



    Kelly H. Zou, Aiyi Liu, Andriy I. Bandos, Lucila Ohno-Machado, Howard E. Rockette

    "… a useful addition to the ROC literature, which will prove valuable for both those involved in medical diagnosis and those whose primary interest is ROC analysis itself."
    —David J. Hand, International Statistical Review (2013), 81, 2

    "This new book by Zou et al significantly contributes to the existing publications by providing short descriptions on basic issues and in-depth presentations on a few advanced, research-related issues. … the interested researcher can get inspired reading this book and discover new, unexplored research paths. Another pro of the book, useful for the interested researcher, is the extensive reference list at the end of each chapter. Overall, the book by Zou et al is a valuable starting point for those conducting basic research on ROC analysis and for applied researchers who are intrigued by the use of neat methodologies in applications."
    ISCB News, June 2012