Impact of unequal cluster sizes for GEE analyses of stepped wedge cluster randomized trials with binary outcomes

Tian, Zibo; Preisser, John S.; Esserman, Denise; Turner, Elizabeth L.; Rathouz, Paul J.; Li, Fan

doi:10.1002/bimj.202100112

Cited by 18 publications

(11 citation statements)

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“…Second, we have assumed that the clusterperiod sizes are identical across clusters and time periods and have not yet enabled power calculation with unequal cluster-period sizes. Unequal cluster-period sizes may be more common in cross-sectional designs, and recent studies showed that varying cluster sizes had a larger impact on statistical power for independence GEE estimators compared with GEE estimators with the correct multilevel correlation structure in SWCRTs (Tian et al 2022). Future versions of our software would consider extensions to unequal cluster-period sizes, based on the efficient Monte Carlo method in Tian et al (2022).…”

Section: Discussionmentioning

confidence: 99%

“…Unequal cluster-period sizes may be more common in cross-sectional designs, and recent studies showed that varying cluster sizes had a larger impact on statistical power for independence GEE estimators compared with GEE estimators with the correct multilevel correlation structure in SWCRTs (Tian et al 2022). Future versions of our software would consider extensions to unequal cluster-period sizes, based on the efficient Monte Carlo method in Tian et al (2022). Finally, we have considered only a complete design where data collection is planned for all cluster periods (Hemming, Lilford, and Girling 2015).…”

Section: Discussionmentioning

confidence: 99%

“…CRTs (Tian et al 2022). Future versions of our software would consider extensions to unequal cluster-period sizes, based on the efficient Monte Carlo method in Tian et al (2022).…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…CRTs (Tian et al 2022). Future versions of our software would consider extensions to unequal cluster-period sizes, based on the efficient Monte Carlo method in Tian et al (2022). Finally, we have considered only a complete design where data collection is planned for all cluster periods .…”

Section: Acknowledgmentsmentioning

confidence: 99%

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power swgee: GEE-based power calculations in stepped wedge cluster randomized trials

Gallis

Wang

Rathouz

et al. 2022

The Stata Journal: Promoting communications on statistics and S

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Stepped wedge cluster randomized trials (SW-CRTs) are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover natures of an SW-CRT, complex analysis techniques are often needed, which makes appropriately powering SW-CRTs challenging. In this article, we introduce a newly developed SW-CRT power calculator embedded within the power command in Stata. The power calculator assumes a marginal model (that is, generalized estimating equations) for the primary analysis of SW-CRTs, for which other currently available SW-CRT power calculators may not be suitable. The command accommodates complete cross-sectional and closed-cohort designs and includes multilevel correlation structures appropriate for such designs. We discuss the methods and formulas underlying our SW-CRT calculator and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgee and conclude by discussing areas of future research that may improve the command.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…CRTs (Tian et al 2022). Future versions of our software would consider extensions to unequal cluster-period sizes, based on the efficient Monte Carlo method in Tian et al (2022).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

See 2 more Smart Citations

power swgee: GEE-based power calculations in stepped wedge cluster randomized trials

Gallis

Wang

Rathouz

et al. 2022

The Stata Journal: Promoting communications on statistics and S

Self Cite

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“…Another advantage in using GEE for CRTs is that the estimation of mean model parameters is robust to misspecification of correlation structures in large samples. However, the specification of working independence correlation structure may result in efficiency loss that can be substantial when the cluster-period sizes are not all equal [Tian et al, 2021]. Furthermore, an over-simplified exchangeable correlation structure may give inaccurate power calculations when there is correlation decay in multi-period CRTs [Li, 2020.…”

Section: Introductionmentioning

confidence: 99%

%CRTFASTGEEPWR: a SAS macro for power of the generalized estimating equations of multi-period cluster randomized trials with application to stepped wedge designs

Zhang¹,

Preisser²,

Li³

et al. 2022

Preprint

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Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) are commonly used to provide population-averaged inference in CRTs, there is a gap of general methods and statistical software tools for power calculation based on multi-parameter, within-cluster correlation structures suitable for multi-period CRTs that can accommodate both complete and incomplete designs. A computationally fast, non-simulation procedure for determining statistical power is described for the GEE analysis of complete and incomplete multi-period cluster randomized trials. The procedure is implemented via a SAS macro, %CRTFASTGEEPWR, which is applicable to binary, count and continuous responses and several correlation structures in multi-period CRTs. The SAS macro is illustrated in the power calculation of two complete and two incomplete stepped wedge cluster randomized trial scenarios under different specifications of marginal mean model and within-cluster correlation structure. The proposed GEE power method is quite general as demonstrated in the SAS macro with numerous input options. The power procedure and macro can also be used in the planning of parallel and crossover CRTs in addition to cross-sectional and closed cohort stepped wedge trials.

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