Accommodating covariates in receiver operating characteristic analysis
AbstractClassification 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 9 (2009)
Issue (Month): 1 (March)
Contact details of provider:
Web page: http://www.stata-journal.com/
You can help add them by filling out this form.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum) or (Lisa Gilmore).
If references are entirely missing, you can add them using this form.