Maintaining the validity of inference in small‐sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations

2020 ◽  
Vol 39 (21) ◽  
pp. 2779-2792
Author(s):  
Whitney P. Ford ◽  
Philip M. Westgate
Keyword(s):  
Generalized Estimating Equations ◽  
Randomized Trials ◽  
Estimating Equations ◽  
Small Sample ◽  
Binary Outcomes ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Cluster Randomized ◽  
Generalized Estimating

scholarly journals Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials

2014 ◽  
Vol 26 (2) ◽  
pp. 583-597 ◽  
Author(s):  
JoAnna M Scott ◽  
Allan deCamp ◽  
Michal Juraska ◽  
Michael P Fay ◽  
Peter B Gilbert
Keyword(s):  
Treatment Effects ◽  
Randomized Trials ◽  
Estimating Equation ◽  
Small Sample ◽  
Generalized Estimating Equation ◽  
Cluster Randomized Trials ◽  
Stepped Wedge ◽  
Population Average ◽  
Cluster Randomized ◽  
Generalized Estimating

Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.

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
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