title: A permutation test sensitive to differences in areas for comparing ROC curves from a paired design.
abstract: The area under the receiver operating characteristic (ROC) curve (AUC) is a widely accepted summary index of the overall performance of diagnostic procedures and the difference between AUCs is often used when comparing two diagnostic systems. We developed an exact non-parametric statistical procedure for comparing two ROC curves in paired design settings. The test which is based on all permutations of the subject specific rank ratings is formally a test for equality of ROC curves that is sensitive to the alternatives of AUC difference. The operating characteristics of the proposed test were evaluated using extensive simulations over a wide range of parameters. The proposed procedure can be easily implemented in experimental ROC data sets. For small samples and for underlying parameters that are common in experimental studies in diagnostic imaging the test possesses good operating characteristics and is more powerful than the conventional non-parametric procedure for AUC comparisons. We also derived an asymptotic version of the test which uses an exact estimate of the variance in the permutation space and provides a good approximation even when the sample sizes are small. This asymptotic procedure is a simple and precise approximation to the exact test and is useful for large sample sizes where the exact test may be computationally burdensome.
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, PA 15261, USA. anb61@pitt.edu
authors.forename: Andriy I
authors.initials: AI
authors.lastname: Bandos
authors.forename: Howard E
authors.initials: HE
authors.lastname: Rockette
authors.forename: David
authors.initials: D
authors.lastname: Gur
mesh_terms: D001699:Biometry; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data; D006801:Humans; D012372:ROC Curve; D013902:Radiography, Thoracic / Q000706:statistics & numerical data; D018709:Statistics, Nonparametric