There is the potential for high‐dimensional information about patients collected in clinical trials (such as genomic, imaging, and data from wearable technologies) to be informative for the efficacy of a new treatment in situations where only a subset of patients benefits from the treatment. The adaptive signature design (ASD) method has been proposed for developing and testing the efficacy of a treatment in a high‐efficacy patient group (the sensitive group) using genetic data. The method requires selection of three tuning parameters which may be highly computationally expensive. We propose a variation to the ASD method, the cross‐validated risk scores (CVRS) design method, that does not require selection of any tuning parameters. The method is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. We assess the properties of CVRS against the originally proposed cross‐validated ASD using simulation data and a real psychiatry trial. CVRS, as assessed for various sample sizes and response rates, has a substantial reduction in the computational time required. In many simulation scenarios, there is a substantial improvement in the ability to correctly identify the sensitive group and the power of the design to detect a treatment effect in the sensitive group. We illustrate the application of the CVRS method on the psychiatry trial.

中文翻译：

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使用风险评分在临床试验中开发和测试高效患者亚组。

在临床试验中收集的有关患者的高维信息（例如基因组、成像和来自可穿戴技术的数据）有可能在只有一部分患者从治疗中受益的情况下为新治疗的疗效提供信息. 自适应特征设计 (ASD) 方法已被提议用于使用遗传数据在高效患者组（敏感组）中开发和测试治疗效果。该方法需要选择三个可能在计算上非常昂贵的调整参数。我们提出了 ASD 方法的一种变体，即交叉验证风险评分 (CVRS) 设计方法，它不需要选择任何调整参数。该方法基于计算每位患者的风险评分，并使用非参数聚类程序将其划分为聚类。我们使用模拟数据和真实的精神病学试验，针对最初提出的交叉验证的 ASD 评估 CVRS 的特性。CVRS，根据不同样本大小和响应率的评估，大大减少了所需的计算时间。在许多模拟场景中，正确识别敏感群体的能力和设计检测敏感群体中治疗效果的能力都有实质性的提高。我们说明了 CVRS 方法在精神病学试验中的应用。根据对各种样本大小和响应率的评估，所需的计算时间显着减少。在许多模拟场景中，正确识别敏感群体的能力和设计检测敏感群体中治疗效果的能力都有实质性的提高。我们说明了 CVRS 方法在精神病学试验中的应用。根据对各种样本大小和响应率的评估，所需的计算时间显着减少。在许多模拟场景中，正确识别敏感群体的能力和设计检测敏感群体中治疗效果的能力都有实质性的提高。我们说明了 CVRS 方法在精神病学试验中的应用。