Assessment of evidence against homogeneity in exhaustive subgroup treatment effect plots

TL;DR

This paper introduces a computational method to assess homogeneity in subgroup treatment effect plots, improving interpretability and statistical inference in clinical trial analyses.

Contribution

It develops a novel, efficient approach using the Doubly Robust learner to identify homogeneity regions and quantify heterogeneity significance in subgroup effects.

Findings

Method provides well-calibrated inference in simulations

Improves over standard mean difference approaches

Applied successfully to a cardiovascular trial

Abstract

Exhaustive subgroup treatment effect plots are constructed by displaying all subgroup treatment effects of interest against subgroup sample size, providing a useful overview of the observed treatment effect heterogeneity in a clinical trial. As in any exploratory subgroup analysis, however, the observed estimates suffer from small sample sizes and multiplicity issues. To facilitate more interpretable exploratory assessments, this paper introduces a computationally efficient method to generate homogeneity regions within exhaustive subgroup treatment effect plots. Using the Doubly Robust (DR) learner, pseudo-outcomes are used to estimate subgroup effects and derive reference distributions, quantifying how surprising observed heterogeneity is under a homogeneous effects model. Explicit formulas are derived for the homogeneity region and different methods for calculation of the critical values are compared. The method is illustrated with a cardiovascular trial and evaluated via simulation, showing well-calibrated inference and improved performance over standard approaches using simple differences of observed group means.