Statistical evaluation of diagnostic performance: topics in ROC analysis

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

Use of likelihood ratios for comparisons of binary diagnostic tests: Underlying ROC curves

Purpose: When comparing binary test results from two diagnostic systems, superiority in both “sensitivity” and “specificity” also implies differences in all conventional summary indices and locally in the underlying receiver operating characteristics (ROC) curves. However, when one of the two binary tests has higher sensitivity and lower specificity (or vice versa), comparisons of their performance levels are nontrivial and the use of different summary indices may lead to contradictory conclusions. A frequently used approach that is free of subjectivity associated with summary indices is based on the comparison of the underlying ROC curves that requires the collection of rating data using multicategory scales, whether natural or experimentally imposed. However, data for reliable estimation of ROC curves are frequently unavailable. The purpose of this article is to develop an approach of using “diagnostic likelihood ratios,” namely, likelihood ratios of “positive” or “negative” responses, to make simple inferences regarding the underlying ROC curves and associated areas in the absence of reliable rating data or regarding the relative binary characteristics, when these are of primary interest