title: A Linear Regression Framework for the Receiver Operating Characteristic (ROC) Curve Analysis.
abstract: The receiver operating characteristic (ROC) curve has been a popular statistical tool for characterizing the discriminating power of a classifier, such as a biomarker or an imaging modality for disease screening or diagnosis. It has been recognized that the accuracy of a given procedure may depend on some underlying factors, such as subject's demographic characteristics or disease risk factors, among others. Non-parametric- or parametric-based methods tend to be either inefficient or cumbersome when evaluating effect of multiple covariates is the main focus. Here we propose a semi-parametric linear regression framework to model covariate effect. It allows the estimation of sensitivity at given specificity to vary according to the covariates and provides a way to model the area under the ROC curve indirectly. Estimation procedure and asymptotic theory are presented. Extensive simulation studies have been conducted to investigate the validity of the proposed method. We illustrate the new method on a diagnostic test dataset.
authors.affiliation: Department of Biostatistics and Center for Statistical Sciences, Brown University, Providence, RI 02912, USA.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Zhang
authors.forename: Ying
authors.initials: Y
authors.lastname: Huang
keywords: AUC; Covariate effect; Linear regression; ROC curve; Sensitivity