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Model-Robust Standardization in Cluster-Randomized Trials.

Fan Li1,2, Jiaqi Tong1,2, Xi Fang1,2

  • 1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.

Statistics in Medicine
|September 19, 2025
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Summary
This summary is machine-generated.

This study introduces a robust method for analyzing cluster-randomized trials, ensuring accurate treatment effect estimation even with model misspecification or informative cluster sizes. The approach provides consistent estimators for both cluster-average and individual-average treatment effects.

Keywords:
covariate‐constrained randomizationgeneralized estimating equationsgeneralized linear mixed modelsinformative cluster sizejackknifemarginal estimands

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Generalized linear mixed models and generalized estimating equations are standard for cluster-randomized trials.
  • These conventional methods can yield ambiguous treatment effect estimates with model misspecification or informative cluster sizes.

Purpose of the Study:

  • To present a unified, model-robust approach for estimand-aligned inference in cluster-randomized trials.
  • To develop consistent estimators for cluster-average and individual-average treatment effects.

Main Methods:

  • A novel standardization approach to align regression model output with estimands.
  • Introduction of always-consistent estimators for marginal treatment effects.
  • Exploration of a deletion-based jackknife variance estimator.
  • Development of a test for informative cluster size.

Main Results:

  • The proposed estimators ensure consistent inference for treatment effects, irrespective of model specification accuracy.
  • The approach provides a reliable method for handling informative cluster sizes.
  • Simulation studies confirm the advantages of the proposed estimators across various scenarios.

Conclusions:

  • The developed model-robust standardization methods offer reliable and consistent estimation of treatment effects in cluster-randomized trials.
  • The MRStdCRT R package implements these novel statistical approaches for practical application.