SEMIPARAMETRIC ROC ANALYSIS USING ACCELERATED REGRESSION MODELS

The Receiver Operating Characteristic (ROC) curve is a widely used measure to assess the diagnostic accuracy of biomarkers for diseases. Biomarker tests can be affected by subject characteristics, the experience of testers, or the environment in which tests are carried out, so it is important to understand and determine the conditions for evaluating biomarkers. In this paper, we focus on assessing the effects of covariates on the performance of the ROC curves. In particular, we develop an accelerated ROC model by assuming that the effect of covariates relates to rescaling a baseline ROC curve. The proposed model generalizes the accelerated failure time model in the survival context to ROC analysis. An innovative method is developed to construct estimation and inference for model parameters. The obtained parameter estimators are shown to be asymptotically normal. We demonstrate the proposed method via a number of simulation studies, and apply it to analyze data from a prostate cancer study.