Accommodating covariates in receiver operating characteristic analysis
Abstract.
Classification accuracy is the ability of a marker or diagnostic test to
discriminate between two groups of individuals, cases and controls, and is
commonly 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.
View all articles by these authors:
Holly Janes, Gary Longton, Margaret S. Pepe
View all articles with these keywords:
roccurve, comproc, rocreg, receiver operating characteristic analysis, ROC, covariates, sensitivity, specificity
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