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

TL;DR

This paper extends the cross-validated risk scores (CVRS) method to jointly analyze two outcomes, enabling identification of patient subgroups benefiting on both, demonstrated through simulations and a psychiatry trial.

Contribution

The paper introduces CVRS2, a novel extension of CVRS for two outcomes, improving subgroup detection in high-dimensional data settings.

Findings

CVRS2 reliably identifies sensitive groups in simulated data.

Application to a psychiatry trial shows CVRS2 detects treatment effects on both outcomes.

CVRS2 outperforms the original CVRS in multi-outcome scenarios.

Abstract

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 non-parametric clustering procedure. In some settings it is desirable to consider the trade-off 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 randomised 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 pre-filtering the covariates.