Assessment of evidence against homogeneity in exhaustive subgroup treatment effect plots

2026-02-06

AI Summary

This study addresses the limitations of existing exhaustive subgroup treatment effect plots, which struggle to reliably assess heterogeneity under small sample sizes and multiple testing, and lack a formal quantification of the significance of observed heterogeneity under the null hypothesis of homogeneous treatment effects. The authors propose a computationally efficient strategy to construct homogeneity regions by leveraging a Doubly Robust learner to generate pseudo-outcomes for subgroup effect estimation. By constructing a reference distribution under homogeneity, the method provides the first framework to quantify evidence of heterogeneity directly within exhaustive subgroup plots. An explicit formula for homogeneity regions is derived, accompanied by several approaches for computing critical thresholds. Empirical evaluations in cardiovascular clinical trials and simulation studies demonstrate well-calibrated performance and substantial improvements over conventional methods based on subgroup mean differences.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• subgroup treatment effect
• homogeneity
• heterogeneity
• exploratory subgroup analysis
• clinical trial

Innovation

• homogeneity region
• exhaustive subgroup treatment effect plot
• Doubly Robust learner
• pseudo-outcomes
• treatment effect heterogeneity