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Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.

Lara Maleyeff^1,2, Fan Li^3,4, Sebastien Haneuse²

¹Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada.

Statistical Methods in Medical Research

|June 17, 2025

Summary

New permutation tests effectively detect treatment effect heterogeneity in cluster randomized trials. These methods improve subgroup analysis in healthcare research, offering greater accuracy than traditional interaction tests.

Keywords:

Cluster randomized trials estimands generalized additive mixed model intracluster correlation coefficient linear mixed models permutation test

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Area of Science:

Biostatistics
Clinical Trials
Health Services Research

Background:

Cluster randomized trials (CRTs) are vital for evaluating healthcare interventions.
Assessing treatment effect heterogeneity across subgroups is crucial but challenging in CRTs.
Conventional methods often rely on parametric assumptions that may not be suitable.

Purpose of the Study:

To develop and validate novel permutation tests for assessing treatment effect heterogeneity in CRTs.
To clarify causal definitions related to effect modification in the context of CRTs.
To provide a robust method for subgroup analysis in CRTs.

Main Methods:

Developed modified permutation tests adapted for the complexities of CRTs.
Clarified causal definitions for treatment effect heterogeneity in CRTs.
Evaluated performance through simulation studies and application to the PPACT study.

Main Results:

The proposed permutation tests maintain nominal type I error rates and exhibit reasonable power.
These methods successfully detected treatment effect heterogeneity in the PPACT study.
The new procedures identified heterogeneity missed by conventional interaction term analyses.

Conclusions:

The developed permutation tests offer a powerful and flexible approach for assessing treatment effect heterogeneity in CRTs.
These methods address limitations of traditional parametric approaches.
The findings enhance the ability to conduct nuanced subgroup analyses in healthcare research.