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Biostatistics 3:421-432 (2002)
© 2002 Oxford University Press

Distribution-free ROC analysis using binary regression techniques

Todd A. Alonzo* and Margaret Sullivan Pepe

Children's Oncology Group, Keck School of Medicine, University of Southern California, PO Box 60012, Arcadia, CA 91066, USA talonzo{at}childrensoncologygroup.org
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA mspepe{at}u.washington.edu

*To whom correspondence should be addressed

Receiver operating characteristic (ROC) regression methodology is used to identify factors that affect the accuracy of medical diagnostic tests. In this paper, we consider a ROC model for which the ROC curve is a parametric function of covariates but distributions of the diagnostic test results are not specified. Covariates can be either common to all subjects or specific to those with disease. We propose a new estimation procedure based on binary indicators defined by the test result for a diseased subject exceeding various specified quantiles of the distribution of test results from non-diseased subjects with the same covariate values. This procedure is conceptually and computationally simplified relative to existing procedures. Simulation study results indicate that the approach has fairly high statistical efficiency. The new ROC regression methodology is used to evaluate childhood measurements of body mass index as a predictive marker of adult obesity.

Keywords: Biomarkers; Classification; Diagnostic tests; Prediction; ROC analysis; Sensitivity; Specificity


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