论文标题
使用关节置信区间确定多个结果中的异质治疗效果
Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals
论文作者
论文摘要
在随机对照试验（RCT）期间通常鉴定出异质的治疗效果（HTE）。鉴定具有相似治疗效果的患者的亚组在临床研究中具有很高的兴趣，以促进精确医学。通常，在RCT期间测量了多个临床结果，每种结局具有潜在的异质作用。最近，人们对识别HTE的亚组的兴趣很高，但是，在有多个结果的设置中开发工具的关注较少。在这项工作中，我们提出了一个框架，用于分区协变空间，以基于联合顺式识别多个结果的亚组。我们在有两个结果的合成和半合成数据上测试算法，并证明我们的算法能够同时捕获两个结果中的HTE。
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.
