A marginal model approach for analysis of multi-reader multi-test receiver operating characteristic (ROC) data
Department of Biostatistics, University of Washington, Box 357232, 1705 NE Pacific Street, Seattle, WA 98195, USA xsong{at}stat.ncsu.edu
HSR&D, VA Puget Sound Health Care System, 1660 S Columbian Way, Seattle, WA 98018, USA and Department of Biostatistics, University of Washington, Box 357232, 1705 NE Pacific Street, Seattle, WA 98195, USA azhou{at}u.washington.edu
The receiver operating characteristic curve is a popular tool to characterize the capabilities of diagnostic tests with continuous or ordinal responses. One common design for assessing the accuracy of diagnostic tests involves multiple readers and multiple tests, in which all readers read all test results from the same patients. This design is most commonly used in a radiology setting, where the results of diagnostic tests depend on a radiologist's subjective interpretation. The most widely used approach for analyzing data from such a study is the Dorfman–Berbaum–Metz (DBM) method (Dorfman et al., 1992) which utilizes a standard analysis of variance (ANOVA) model for the jackknife pseudovalues of the area under the ROC curves (AUCs). Although the DBM method has performed well in published simulation studies, there is no clear theoretical basis for this approach. In this paper, focusing on continuous outcomes, we investigate its theoretical basis. Our result indicates that the DBM method does not satisfy the regular assumptions for standard ANOVA models, and thus might lead to erroneous inference. We then propose a marginal model approach based on the AUCs which can adjust for covariates as well. Consistent and asymptotically normal estimators are derived for regression coefficients. We compare our approach with the DBM method via simulation and by an application to data from a breast cancer study. The simulation results show that both our method and the DBM method perform well when the accuracy of tests under the study is the same and that our method outperforms the DBM method for inference on individual AUCs when the accuracy of tests is not the same. The marginal model approach can be easily extended to ordinal outcomes.
Keywords: ANOVA; AUC; GEE; Marginal model; ROC; Sparse correlation