Developing a predictive signature for two trial endpoints using the cross-validated risk scores method

The existing cross-validated risk scores (CVRS) design has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using high-dimensional data (such as genetic data). The design is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. In some settings, it is desirable to consider the tradeoff between two outcomes, such as efficacy and toxicity, or cost and effectiveness. With this motivation, we extend the CVRS design (CVRS2) to consider two outcomes. The design employs bivariate risk scores that are divided into clusters. We assess the properties of the CVRS2 using simulated data and illustrate its application on a randomized psychiatry trial. We show that CVRS2 is able to reliably identify the sensitive group (the group for which the new treatment provides benefit on both outcomes) in the simulated data. We apply the CVRS2 design to a psychology clinical trial that had offender status and substance use status as two outcomes and collected a large number of baseline covariates. The CVRS2 design yields a significant treatment effect for both outcomes, while the CVRS approach identified a significant effect for the offender status only after prefiltering the covariates.

Automated summary

The CVRS2 design is a new approach to develop and test the effectiveness of treatment in high-dimensional data. It considers the tradeoff between two outcomes and divides patients into clusters using bivariate risk scores. Through simulated data and a real clinical trial, we demonstrate the reliability and applicability of the CVRS2 design.