Subgroup Identification using Covariate Adjusted Interaction Trees

TL;DR

This paper introduces covariate adjusted estimators for recursive partitioning to improve subgroup identification in clinical trials, enhancing efficiency when covariates are prognostic, demonstrated through simulations and a real trial analysis.

Contribution

It develops two covariate adjusted estimators for better splitting and final tree selection in recursive partitioning for subgroup detection.

Findings

Covariate adjustment improves subgroup detection efficiency.

Simulation results show enhanced performance over traditional methods.

Application to a clinical trial demonstrates practical utility.

Abstract

We consider the problem of identifying sub-groups of participants in a clinical trial that have enhanced treatment effect. Recursive partitioning methods that recursively partition the covariate space based on some measure of between groups treatment effect difference are popular for such sub-group identification. The most commonly used recursive partitioning method, the classification and regression tree algorithm, first creates a large tree by recursively partitioning the covariate space using some splitting criteria and then selects the final tree from all subtrees of the large tree. In the context of subgroup identification, calculation of the splitting criteria and the evaluation measure used for final tree selection rely on comparing differences in means between the treatment and control arm. When covariates are prognostic for the outcome, covariate adjusted estimators have the ability to improve efficiency compared to using differences in means between the treatment and control group. This manuscript develops two covariate adjusted estimators that can be used to both make splitting decisions and for final tree selection. The performance of the resulting covariate adjusted recursive partitioning algorithm is evaluated using simulations and by analyzing a clinical trial that evaluates if motivational interviews improve treatment engagement for substance abusers.