ArticleStatistics in medicine2019
Optimal designs in three-level cluster randomized trials with a binary outcome.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Optimal Designs in Open-Cohort Longitudinal Cluster Randomized Trials With a Continuous Outcome.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Identifying Less Burdensome and More Cost-Efficient Incomplete Stepped Wedge Designs for Continuous Outcomes Collected via Repeated Cross-Sections.Statistics in medicine · 2025Article
- Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials.Statistical methods in medical research · 2024Article
- Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.Statistics in medicine · 2023Article
- An overview of optimal designs under a given budget in cluster randomized trials with a binary outcome.Statistical methods in medical research · 2023Review
- Power considerations for generalized estimating equations analyses of four-level cluster randomized trials.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Sample size calculation in three-level cluster randomized trials using generalized estimating equation models.Statistics in medicine · 2020Article
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Authors and funding
3 authors.
Funding
Abstract
Cluster randomized trials (CRTs) were originally proposed for use when randomization at the subject level is practically infeasible or may lead to a severe estimation bias of the treatment effect. However, recruiting an additional cluster costs more than enrolling an additional subject in an individually randomized trial. Under budget constraints, researchers have proposed the optimal sample sizes in two-level CRTs. CRTs may have a three-level structure, in which two levels of clustering should be considered. In this paper, we propose optimal designs in three-level CRTs with a binary outcome, assuming a nested exchangeable correlation structure in generalized estimating equation models. We provide the variance of estimators of three commonly used measures: risk difference, risk ratio, and odds ratio. For a given sampling budget, we discuss how many clusters and how many subjects per cluster are necessary to minimize the variance of each measure estimator. For known association parameters, the locally optimal design is proposed. When association parameters are unknown but within predetermined ranges, the MaxiMin design is proposed to maximize the minimum of relative efficiency over the possible ranges, that is, to minimize the risk of the worst scenario.
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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.