Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals
This work addresses a gap in precision medicine by developing tools for subgroup identification when multiple outcomes are measured, which is incremental as it builds on existing HTE methods.
The authors tackled the problem of identifying heterogeneous treatment effects across multiple clinical outcomes in randomized controlled trials, proposing a framework that partitions covariate space using joint confidence intervals and demonstrating its ability to capture effects in both outcomes simultaneously on synthetic and semi-synthetic data.
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.