Targeting relative risk heterogeneity with causal forests

TL;DR

This paper introduces a modified causal forest method that targets relative risk heterogeneity, improving detection of treatment effect variability in clinical trial data.

Contribution

It proposes a novel node-splitting procedure based on generalized linear model comparison to focus causal forests on relative risk heterogeneity.

Findings

Relative risk causal forests detect hidden heterogeneity.

Method performs well on simulated data.

Applied to real trial data for liraglutide.

Abstract

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.