Combining biomarkers for classification with covariate adjustment.

ABSTRACT: Combining multiple markers can improve classification accuracy compared with using a single marker. In practice, covariates associated with markers or disease outcome can affect the performance of a biomarker or biomarker combination in the population. The covariate-adjusted receiver operating characteristic (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker; this curve characterizes the classification accuracy solely because of the marker of interest. However, research on the effect of covariates on the performance of marker combinations and on how to adjust for the covariate effect when combining markers is still lacking. In this article, we examine the effect of covariates on classification performance of linear marker combinat

SUBMITTER: Kim S 

PROVIDER: S-EPMC5459674 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature