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Zusatztext "? 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 Informationen zum Autor Kelly H. Zou, Aiyi Liu, Andriy I. Bandos, Lucila Ohno-Machado, Howard E. Rockette Klappentext An important method for statistical validation is the receiver operating characteristic (ROC) analysis. This visual tool is used in a variety of clinical areas, including laboratory testing, epidemiology, radiology, and bioinformatics, for evaluating diagnostic tests. This book gives a historical overview of the empirical and nonparametric ROC method for continuous diagnostic and classification data. It introduces methods for estimating and comparing ROC curves based on diagnostic test results and covers both semiparametric and parametric models. The authors develop likelihood-based algorithms for estimating an ROC curve and its characteristics under these models. They also present methods for sample size calculations and Monte Carlo simulations. The text includes many real clinical examples, with R code provided for all of them. Zusammenfassung 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. Inhaltsverzeichnis 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. ...
