ArticleJournal of biopharmaceutical statistics2021
Relative efficiency of equal versus unequal cluster sizes in cluster randomized trials with a small number of clusters.
Article in Journal of biopharmaceutical statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Selecting the optimal longitudinal cluster randomized design with a continuous outcome: Parallel-arm, crossover, or stepped-wedge.Statistical methods in medical research · 2025Article
- Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review.PloS one · 2021Article
- Sample size calculation in three-level cluster randomized trials using generalized estimating equation models.Statistics in medicine · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
To calculate sample sizes in cluster randomized trials (CRTs), the cluster sizes are usually assumed to be identical across all clusters for simplicity. However, equal cluster sizes are not guaranteed in practice, especially when the number of clusters is limited. Therefore, it is important to understand the relative efficiency (RE) of equal versus unequal cluster sizes when designing CRTs with a limited number of clusters. In this paper, we are interested in the RE of two bias-corrected sandwich estimators of the treatment effect in the Generalized Estimating Equation (GEE) models for CRTs with a small number of clusters. Specifically, we derive the RE of two bias-corrected sandwich estimators for binary, continuous, or count data in CRTs under the assumption of an exchangeable working correlation structure. We consider different scenarios of cluster size distributions and investigate RE performance through simulation studies. We conclude that the number of clusters could be increased by as much as 42% to compensate for efficiency loss due to unequal cluster sizes. Finally, we propose an algorithm of increasing the number of clusters when the coefficient of variation of cluster sizes is known and unknown.
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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.