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

2021 ◽  
Author(s):  
Zibo Tian ◽  
John S. Preisser ◽  
Denise Esserman ◽  
Elizabeth L. Turner ◽  
Paul J. Rathouz ◽  
...  
Keyword(s):  
Randomized Trials ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Cluster Randomized ◽  
Unequal Cluster

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

2021 ◽  
Author(s):  
Zibo Tian ◽  
John Preisser ◽  
Denise Esserman ◽  
Elizabeth Turner ◽  
Paul Rathouz ◽  
...  
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Washington State ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Study Planning ◽  
Working Correlation ◽  
Cluster Randomized ◽  
Unequal Cluster

The stepped wedge design is a type of unidirectional crossover design where cluster units switch from control to intervention condition at different pre-specified time points. While a convention in study planning is to assume the cluster-period sizes are identical, stepped wedge cluster randomized trials (SW-CRTs) involving repeated cross-sectional designs frequently have unequal cluster-period sizes, which can impact the efficiency of the treatment effect estimator. In this article, we provide a comprehensive investigation of the efficiency impact of unequal cluster sizes for generalized estimating equation analyses of SW-CRTs, with a focus on binary outcomes as in the Washington State Expedited Partner Therapy trial. Several major distinctions between our work and existing work include: (i) we consider multilevel correlation structures in marginal models with binary outcomes; (ii) we study the implications of both the between-cluster and within-cluster imbalances in sizes; and (iii) we provide a comparison between the independence working correlation versus the true working correlation and detail the consequences of ignoring correlation estimation in SW-CRTs with unequal cluster sizes. We conclude that the working independence assumption can lead to substantial efficiency loss and a large sample size regardless of cluster-period size variability in SW-CRTs, and recommend accounting for correlations in the analysis. To improve study planning, we additionally provide a computationally efficient search algorithm to estimate the sample size in SW-CRTs accounting for unequal cluster-period sizes, and conclude by illustrating the proposed approach in the context of the Washington State study.

scholarly journals A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

2011 ◽  
Vol 8 (6) ◽  
pp. 687-698 ◽  
Author(s):  
Catherine M Crespi ◽  
Weng Kee Wong ◽  
Sheng Wu
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Pearson Correlation ◽  
Sample Size Determination ◽  
Binary Outcomes ◽  
Alternative Measure ◽  
Cluster Randomized Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Measure Of Dependence

Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

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