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.

翻译：现有的交叉验证风险评分（CVRS）设计已被提出用于利用高维数据（如遗传数据）开发并检验治疗在高疗效患者组（敏感组）中的有效性。该设计基于计算每位患者的风险评分，并通过非参数聚类程序将其划分为不同聚类。在某些场景下，需要权衡两个结局指标（如疗效与毒性、成本与效果）。基于这一动机，我们将CVRS设计扩展为CVRS2以纳入两个结局。该设计采用二元风险评分并进行聚类划分。我们通过模拟数据评估CVRS2的特性，并在一项随机精神病学试验中展示其应用。结果表明，CVRS2能够在模拟数据中可靠地识别敏感组（即新疗法在两个结局上均能带来获益的群体）。我们将CVRS2设计应用于一项心理学临床试验，该试验以犯罪状态和物质使用状态作为两个结局，并收集了大量基线协变量。CVRS2设计在两个结局上均产生了显著的治疗效应，而CVRS方法仅在预筛选协变量后才能识别出罪犯状态的显著效应。