ROC Analysis in Biomarker Combination with Covariate Adjustment

Abstract

Receiver operating characteristic (ROC) analysis is often used to find the optimal combination of biomarkers. When the subject level covariates affect the magnitude and/or accuracy of the biomarkers, the combination rule should take into account of the covariate adjustment. The authors propose two new biomarker combination methods that make use of the covariate information. The first method is to maximize the area under the covariate-adjusted ROC curve (AAUC). To overcome the limitations of the AAUC measure, the authors further proposed the area under covariate-standardized ROC curve (SAUC), which is an extension of the covariate-specific ROC curve. With a series of simulation studies, the proposed optimal AAUC and SAUC methods are compared with the optimal AUC method that ignores the covariates. The biomarker combination methods are illustrated by an example from Alzheimer's disease research. The simulation results indicate that the optimal AAUC combination performs well in the current study population. The optimal SAUC method is flexible to choose any reference populations, and allows the results to be generalized to different populations. The proposed optimal AAUC and SAUC approaches successfully address the covariate adjustment problem in estimating the optimal marker combination. The optimal SAUC method is preferred for practical use, because the biomarker combination rule can be easily evaluated for different population of interest.