ArticleStatistics in medicine2022
Accounting for unequal cluster sizes in designing cluster randomized trials to detect treatment effect heterogeneity.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A tutorial on conducting sample size and power calculations for detecting treatment effect heterogeneity in cluster randomized trials with linear mixed models.International journal of epidemiology · 2026Article
- Group Sequential Design and Monitoring of Clustered Data in Randomized Eye Trials.Statistics in biopharmaceutical research · 2025Article
- Brief verbal intervention to address inappropriate prescriptions of chinese patent medicines among western practitioners in primary health care (BRAVERY): a study protocol for an unannounced standardized patient experiment with a factorial design randomized controlled trial in China.BMC complementary medicine and therapies · 2025Article
- Article
- Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials.Clinical trials (London, England) · 2024Article
- Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.Statistical methods in medical research · 2024Article
- Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials.Statistical methods in medical research · 2024Article
- Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.Statistics in medicine · 2024Article
- Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.Statistics in medicine · 2023Article
- Designing three-level cluster randomized trials to assess treatment effect heterogeneity.Biostatistics (Oxford, England) · 2023Article
- Estimating intra-cluster correlation coefficients for planning longitudinal cluster randomized trials: a tutorial.International journal of epidemiology · 2023Article
- Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity.Statistics in medicine · 2023Article
- Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity.BMC medical research methodology · 2023Article
- Estimands in cluster-randomized trials: choosing analyses that answer the right question.International journal of epidemiology · 2023Article
- Health equity considerations in pragmatic trials in Alzheimer's and dementia disease: Results from a methodological review.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
3 authors.
Funding
Abstract
Unequal cluster sizes are common in cluster randomized trials (CRTs). While there are a number of previous investigations studying the impact of unequal cluster sizes on the power for testing the average treatment effect in CRTs, little is known about the impact of unequal cluster sizes on the power for testing the heterogeneous treatment effect (HTE) in CRTs. In this work, we expand the sample size procedures for studying HTE in CRTs to accommodate cluster size variation under the linear mixed model framework. Through analytical derivation and graphical exploration, we show that the sample size for the HTE with an individual-level effect modifier is less affected by unequal cluster sizes than with a cluster-level effect modifier. The impact of cluster size variability jointly depends on the mean and coefficient of variation of cluster sizes, covariate intraclass correlation coefficient (ICC) and the conditional outcome ICC. In addition, we demonstrate that the HTE-motivated analysis of covariance framework can be used for analyzing the average treatment effect, and offer a more efficient sample size procedure for studying the average treatment effect adjusting for the effect modifier. We use simulations to confirm the accuracy of the proposed sample size procedures for both the average treatment effect and HTE in CRTs. Extensions to multivariate effect modifiers are provided and our procedure is illustrated in the context of the Strategies to Reduce Injuries and Develop Confidence in Elders trial.
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