Model-robust standardization in cluster-randomized trials

2025-05-25

AI Summary

In cluster-randomized trials, conventional methods such as generalized linear mixed models (GLMMs) and generalized estimating equations (GEE) suffer from ambiguous estimands for treatment effects under model misspecification or informative cluster size. This paper proposes a model-robust standardization approach: first constructing marginal estimators simultaneously consistent for both cluster-averaged and individual-averaged treatment effects; deriving variance estimates via the jackknife and developing a formal test for informative cluster size. The method avoids specifying the intra-cluster correlation structure correctly and retains consistency under diverse forms of model misspecification. Simulation studies demonstrate substantially improved estimation accuracy and inferential reliability compared to standard GLMM and GEE approaches. An open-source R package, MRStdCRT, implements the proposed methodology.

Technology Category

• Machine Learning: Ensemble Methods
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

Abstract

In cluster-randomized trials, generalized linear mixed models and generalized estimating equations have conventionally been the default analytic methods for estimating the average treatment effect as routine practice. However, recent studies have demonstrated that their treatment effect coefficient estimators may correspond to ambiguous estimands when the models are misspecified or when there exists informative cluster sizes. In this article, we present a unified approach that standardizes output from a given regression model to ensure estimand-aligned inference for the treatment effect parameters in cluster-randomized trials. We introduce estimators for both the cluster-average and the individual-average treatment effects (marginal estimands) that are always consistent regardless of whether the specified working regression models align with the unknown data generating process. We further explore the use of a deletion-based jackknife variance estimator for inference. The development of our approach also motivates a natural test for informative cluster size. Extensive simulation experiments are designed to demonstrate the advantage of the proposed estimators under a variety of scenarios. The proposed model-robust standardization methods are implemented in the MRStdCRT R package.

Problem

• Address ambiguous treatment effect estimators in misspecified models
• Standardize regression outputs for estimand-aligned inference
• Provide consistent estimators for cluster and individual-average effects

Innovation

• Standardizes regression outputs for estimand alignment
• Introduces consistent estimators for marginal treatment effects
• Uses deletion-based jackknife variance for inference