ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2026
On the mixed-model analysis of covariance in cluster-randomized trials.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial.Clinical trials (London, England) · 2023Trial
- On flexible covariate adjustment under covariate-constrained randomization.Clinical trials (London, England) · 2026Article
- Confidence interval estimation for the win probability in cluster randomized trials with hierarchical composite endpoints using win fractions.Clinical trials (London, England) · 2026Article
- Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes.Statistics in medicine · 2026Article
- Domain-specific motor skill outcomes following a structured play-based motor intervention in preschool children: a cluster-randomized controlled trial.Frontiers in sports and active living · 2026Article
- Development and preliminary evaluation of a ciliary muscle-oriented motor imagery script for primary school children: a school-based cluster randomized controlled trial.Frontiers in psychology · 2026Article
- Covariate adjustment in cluster randomised trials: a practical guide.BMJ (Clinical research ed.) · 2025Article
- Designing Stepped Wedge Cluster Randomized Trials With a Baseline Measurement of the Outcome.Statistics in medicine · 2025Article
- Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints on different scales.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
- Article
- 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
- Authors' Reply: Ambiguity in Statistical Analysis Methods and Nonconformity With Prespecified Commitment to Data Sharing in a Cluster Randomized Controlled Trial.Journal of medical Internet research · 2024Article
- Model-robust and efficient covariate adjustment for cluster-randomized experiments.Journal of the American Statistical Association · 2024Article
- Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.Statistics in medicine · 2023Article
- Key considerations for designing, conducting and analysing a cluster randomized trial.International journal of epidemiology · 2023Article
- RANDOMIZATION INFERENCE FOR CLUSTER-RANDOMIZED TEST-NEGATIVE DESIGNS WITH APPLICATION TO DENGUE STUDIES: UNBIASED ESTIMATION, PARTIAL COMPLIANCE, AND STEPPED-WEDGE DESIGN.The annals of applied statistics · 2023Article
- The BNT162b2 mRNA SARS-CoV-2 vaccine induces transient afucosylated IgG1 in naive but not in antigen-experienced vaccinees.EBioMedicine · 2023Article
- Constrained randomization and statistical inference for multi-arm parallel cluster randomized controlled trials.Statistics in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In the analyses of cluster-randomized trials, mixed-model analysis of covariance (ANCOVA) is a standard approach for covariate adjustment and handling within-cluster correlations. However, when the normality, linearity, or the random-intercept assumption is violated, the validity and efficiency of the mixed-model ANCOVA estimators for estimating the average treatment effect remain unclear. Under the potential outcomes framework, we prove that the mixed-model ANCOVA estimators for the average treatment effect are consistent and asymptotically normal under arbitrary misspecification of its working model. If the probability of receiving treatment is 0.5 for each cluster, we further show that the model-based variance estimator under mixed-model ANCOVA1 (ANCOVA without treatment-covariate interactions) remains consistent, clarifying that the confidence interval given by standard software is asymptotically valid even under model misspecification. Beyond robustness, we discuss several insights on precision among classical methods for analyzing cluster-randomized trials, including the mixed-model ANCOVA, individual-level ANCOVA, and cluster-level ANCOVA estimators. These insights may inform the choice of methods in practice. Our analytical results and insights are illustrated via simulation studies and analyses of three cluster-randomized trials.
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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 OpenQuestion graph.