Application of a Beta Regression Model for Covariate Adjusted ROC

Abstract

The Receiver Operating Characteristic (ROC) curve and the area under the ROC (AUC) are widely used in determining the diagnostic capability of a binary classification procedure. Since the test performance is affected by covariates, the ROC and AUC have been utilized in a Generalized Linear Regression (GLM) setting. In this study, we revisit a problem where the AUC regression model was used in a clinical study with discrete covariates by considering ROC regression models with both discrete and continuous covariates. The two ROC regression models are based upon a widely used parametric model and a recently published model based upon fitting the placement values with the beta distribution. The two methods are illustrated using data from a clinic study.