ArticleStatistical methods in medical research2024
Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs.
Article in Statistical methods in medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The design of embedded pragmatic clinical trials: methodological developments and statistical lessons learned from the first cycle of the NIA IMPACT collaboratory.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- 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
- Cluster minimal sufficient balance (CMSB): an efficient covariate balancing randomization method for cluster randomized trials.BMC medical research methodology · 2026Article
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Authors and funding
5 authors.
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Abstract
The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel-arm cluster randomized design. While statistical methods have been developed for designing cluster randomized crossover trials, they have exclusively focused on testing the overall average treatment effect, with little attention to differential treatment effects across subpopulations. Recently, interest has grown in understanding whether treatment effects may vary across pre-specified patient subpopulations, such as those defined by demographic or clinical characteristics. In this article, we consider the two-treatment two-period cluster randomized crossover design under either a cross-sectional or closed-cohort sampling scheme, where it is of interest to detect the heterogeneity of treatment effect via an interaction test. Assuming a patterned correlation structure for both the covariate and the outcome, we derive new sample size formulas for testing the heterogeneity of treatment effect with continuous outcomes based on linear mixed models. Our formulas also address unequal cluster sizes and therefore allow us to analytically assess the impact of unequal cluster sizes on the power of the interaction test in cluster randomized crossover designs. We conduct simulations to confirm the accuracy of the proposed methods, and illustrate their application in two real cluster randomized crossover trials.
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