ArticleStatistics in medicine2024
Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate adjustment: A simulation study.
Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Offering nurse care management for opioid use disorder in primary care: Impact on emergency and hospital utilization in a cluster-randomized implementation trial.Drug and alcohol dependence · 2024Trial
- A Novel Pixel-Based Spatial Modeling Framework for Mapping At-Risk Retina in Geographic Atrophy Progression.Ophthalmology science · 2026Article
- Determining the Impact of a Physiotherapist-Led Primary Care Model for Low Back Pain: Protocol and Analysis Plan for a Cluster Randomized Controlled Trial and Embedded Process Evaluation.JMIR research protocols · 2026Article
- Increased risk of type I errors for detecting heterogeneity of treatment effects in cluster-randomized trials using mixed-effect models.BMC medical research methodology · 2026Article
- Using reporting guidelines to improve the reproducibility of cooking Christmas tree meringues: the "People tasting trees" cluster-randomised controlled trial.Research integrity and peer review · 2025Article
- Covariate adjustment in cluster randomised trials: a practical guide.BMJ (Clinical research ed.) · 2025Article
- Article
- Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial.JAMA internal medicine · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate-adjusted models remains unknown. An important setting in which this issue arises is in cluster-randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM-based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel-group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person-level or cluster-level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non-negligible (
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