2018
DOI: 10.1080/03610926.2018.1532004
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Sample size calculation for count outcomes in cluster randomization trials with varying cluster sizes

Abstract: In many cluster randomization studies, cluster sizes are not fixed and may be highly variable. For those studies, sample size estimation assuming a constant cluster size may lead to under-powered studies. Sample size formulas have been developed to incorporate the variability in cluster size for clinical trials with continuous and binary outcomes. Count outcomes frequently occur in cluster randomized studies. In this paper, we derive a closed-form sample size formula for count outcomes accounting for the varia… Show more

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Cited by 9 publications
(21 citation statements)
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“…There are several existing sample size formulas developed for analyzing clustered count outcomes, all of which assume the absence of truncation (Amatya et al., 2013; Hayes & Moulton, 2009; Wang et al., 2020). Ogungbenro and Aarons (2010) obtained a closed‐form power formula for longitudinal pharmacodynamic studies with repeated count measurements based on the Poisson random‐effects model.…”
Section: Introductionmentioning
confidence: 99%
“…There are several existing sample size formulas developed for analyzing clustered count outcomes, all of which assume the absence of truncation (Amatya et al., 2013; Hayes & Moulton, 2009; Wang et al., 2020). Ogungbenro and Aarons (2010) obtained a closed‐form power formula for longitudinal pharmacodynamic studies with repeated count measurements based on the Poisson random‐effects model.…”
Section: Introductionmentioning
confidence: 99%
“…In this article, we propose a sample size calculation method for count data in CRTs. Compared with existing methods, 8,12 it is advantageous in directly incorporating pragmatic features including overdispersion, varying cluster sizes, varying lengths of patient's follow‐up, and arbitrary randomization ratios. Furthermore, the sample size formula retains a closed form, facilitating its wide implementation by practitioners.…”
Section: Resultsmentioning
confidence: 99%
“…Many researchers have shown that ignoring cluster size variability in sample size calculation can lead to underpowered studies 9‐11 . Wang et al 12 proposed to incorporate cluster size variability into sample size calculation for CRTs with count outcomes, where a correction term defined based on the coefficient of variation in cluster size is included 13 . The relative efficiency of unequal vs equal cluster sizes when testing the treatment effect in two‐arm CRTs was investigated continuous, binary and count outcomes.…”
Section: Introductionmentioning
confidence: 99%
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