Predictive signature development based on maximizing the area between receiver operating characteristic curves

Development of marker signatures to predict treatment benefits for a new therapeutic is an important scientific component in advancing the drug discovery and is an important first step toward the goal of precision medicine. In this article, we focus on developing an algorithm to search for optimal linear combination of markers that maximizes the area between two receiver operating characteristic curves of the new therapeutic and the control groups without assuming any parametric model. We further generalize the proposed algorithm for predictive signature development to maximize the difference of Harrel's C-index of the new therapeutic and the control groups when the outcome of interest is time-to-event. The performance of this proposed method is evaluated and compared to existing methods via simulations and real clinical trial data.