Targeting relative risk heterogeneity with causal forests
This work addresses the need for more clinically relevant subgroup analysis in medical trials, though it is incremental as it builds on existing causal forest methods.
The authors tackled the problem of identifying heterogeneous treatment effects in clinical trials by modifying causal forests to target relative risk instead of absolute risk, resulting in a method that captured previously undetected heterogeneity in simulated and real-world data.
The identification of heterogeneous treatment effects (HTE) across subgroups is of significant interest in clinical trial analysis. Several state-of-the-art HTE estimation methods, including causal forests, apply recursive partitioning for non-parametric identification of relevant covariates and interactions. However, the partitioning criterion is typically based on differences in absolute risk. This can dilute statistical power by masking variation in the relative risk, which is often a more appropriate quantity of clinical interest. In this work, we propose and implement a methodology for modifying causal forests to target relative risk, using a novel node-splitting procedure based on exhaustive generalized linear model comparison. We present results from simulated data that suggest relative risk causal forests can capture otherwise undetected sources of heterogeneity. We implement our method on real-world trial data to explore HTEs for liraglutide in patients with type 2 diabetes.