Semiparametric estimation of the covariate-specific ROC curve in presence of ignorable verification bias.

ABSTRACT: Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this article, we propose a semiparametric estimation of the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the ROC curve both have flexible shapes. With the gold standard missing at random (MAR) assumption, we consider weighted

SUBMITTER: Liu D 

PROVIDER: S-EPMC3596883 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature