Accommodating covariates in receiver operating characteristic analysis

Holly Janes (), Gary Longton () and Margaret S. Pepe ()
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Holly Janes: Fred Hutchinson Cancer Research Center
Gary Longton: Fred Hutchinson Cancer Research Center
Margaret S. Pepe: Fred Hutchinson Cancer Research Center

Stata Journal, 2009, vol. 9, issue 1, 17-39

Abstract: Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is com- monly summarized by using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three ways of using covariate information. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and we ask how much discriminatory accuracy improves (in incremental value) with the addition of the marker to the covariates. These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution. Copyright 2009 by StataCorp LP.

Keywords: roccurve; comproc; rocreg; receiver operating characteristic analysis; ROC; covariates; sensitivity; specificity (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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