使用联合置信区间识别多个结局中的异质性治疗效应
Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals
摘要：
随机对照试验（RCTs）中通常会发现异质性治疗效果（HTE）。识别具有相似治疗效果的患者亚组在推进精准医学的临床研究中具有很高的兴趣。通常，在RCT期间测量多个临床结局，每个结局具有潜在的异质性效应。最近有很高的兴趣，确定亚组从高技术性经济体，但是，有较少的重点放在开发工具的设置，有多种结果。在这项工作中，我们提出了一个框架，用于划分协变量空间，以确定基于联合CI的多个结果的子组。我们测试我们的算法在合成和半合成数据，有两个结果，并证明我们的算法是能够捕获的HTE在两个结果同时。
英文摘要：
Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often, multiple clinical outcomes are measured during an RCT, each having a potentially heterogeneous effect. Recently there has been high interest in identifying subgroups from HTEs, however, there has been less focus on developing tools in settings where there are multiple outcomes. In this work, we propose a framework for partitioning the covariate space to identify subgroups across multiple outcomes based on the joint CIs. We test our algorithm on synthetic and semi-synthetic data where there are two outcomes, and demonstrate that our algorithm is able to capture the HTE in both outcomes simultaneously.
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