Optimal Composite Markers for Time-Dependent Receiver Operating Characteristic Curves with Censored Survival Data

To increase the predictive abilities of several plasma biomarkers on the coronary artery disease (CAD)-related vital statuses over time, our research interest mainly focuses on seeking combinations of these biomarkers with the highest time-dependent receiver operating characteristic curves. An extended generalized linear model (EGLM) with time-varying coefficients and an unknown bivariate link function is used to characterize the conditional distribution of time to CAD-related death. Based on censored survival data, two non-parametric procedures are proposed to estimate the optimal composite markers, linear predictors in the EGLM model. Estimation methods for the classification accuracies of the optimal composite markers are also proposed. In the article we establish theoretical results of the estimators and examine the corresponding finite-sample properties through a series of simulations with different sample sizes, censoring rates and censoring mechanisms. Our optimization procedures and estimators are further shown to be useful through an application to a prospective cohort study of patients undergoing angiography.