ArticleJournal of the American Statistical Association2024
Model-robust and efficient covariate adjustment for cluster-randomized experiments.
Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- What is estimated in cluster randomized crossover trials with informative sizes? A survey of estimands and common estimators.Statistical methods in medical research · 2026Article
- On flexible covariate adjustment under covariate-constrained randomization.Clinical trials (London, England) · 2026Article
- Principal stratification with U-statistics under principal ignorability.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- CRT-Estimands Framework: consensus based extension of the ICH E9(R1) addendum for cluster randomised trials.BMJ (Clinical research ed.) · 2026Article
- Use of estimands in cluster randomised trials: A review.Clinical trials (London, England) · 2026Review
- Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes.Statistics in medicine · 2026Article
- On the mixed-model analysis of covariance in cluster-randomized trials.Statistical science : a review journal of the Institute of Mathematical Statistics · 2026Article
- Article
- Covariate adjustment in cluster randomised trials: a practical guide.BMJ (Clinical research ed.) · 2025Article
- Permutation tests for detecting treatment effect heterogeneity in cluster randomized trials.Statistical methods in medical research · 2025Article
- How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?-A Survey of Estimands and Common Estimators.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Weighting methods for truncation by death in cluster-randomized trials.Statistical methods in medical research · 2025Article
- Transcriptional Patterns of Nodal Entropy Abnormalities in Major Depressive Disorder Patients with and without Suicidal Ideation.Research (Washington, D.C.) · 2025Article
- How to achieve model-robust inference in stepped wedge trials with model-based methods?Biometrics · 2024Article
- Demystifying estimands in cluster-randomised trials.Statistical methods in medical research · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment remains unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this article, we first adapt two model-based methods-generalized estimating equations and linear mixed models-with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. To further overcome the limitations of model-based covariate adjustment methods, we propose efficient estimators for each estimand that allow for flexible covariate adjustment and additionally address cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. For our proposed covariate-adjusted estimators, we prove that when the nuisance functions are consistently estimated by machine learning algorithms, the estimators are consistent, asymptotically normal, and efficient. When the nuisance functions are estimated via parametric working models, the estimators are triply-robust. Simulation studies and analyses of three real-world cluster-randomized experiments demonstrate that the proposed methods are superior to existing alternatives.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.