Abstract
Generalised estimating equations with the sandwich standard-error estimator provide a promising method of analysis for stepped wedge cluster randomised trials. However, they have inflated type-one error when used with a small number of clusters, which is common for stepped wedge cluster randomised trials. We present a large simulation study of binary outcomes comparing bias-corrected standard errors from Fay and Graubard; Mancl and DeRouen; Kauermann and Carroll; Morel, Bokossa, and Neerchal; and Mackinnon and White with an independent and exchangeable working correlation matrix. We constructed 95% confidence intervals using a t-distribution with degrees of freedom including clusters minus parameters (DFC-P), cluster periods minus parameters, and estimators from Fay and Graubard (DFFG), and Pan and Wall. Fay and Graubard and an approximation to Kauermann and Carroll (with simpler matrix inversion) were unbiased in a wide range of scenarios with an independent working correlation matrix and more than 12 clusters. They gave confidence intervals with close to 95% coverage with DFFG with 12 or more clusters, and DFC-P with 18 or more clusters. Both standard errors were conservative with fewer clusters. With an exchangeable working correlation matrix, approximated Kauermann and Carroll and Fay and Graubard had a small degree of under-coverage.
Highlights
Many small sample corrections are available for generalised estimating equations[1] (GEE) with the sandwich standard-error estimator,[2,3,4,5,6,7] but there has been limited research on which are appropriate for use in stepped wedge trials (SW-cluster randomised trial (CRT)) with a binary outcome
We found that Kauermann and Carroll (KC) standard errors were unbiased when used with an independent working correlation matrix with cluster size from 24 to 300, Coefficient of variation (CV) of cluster size 0 to 0.4, intracluster correlation coefficient (ICC) from 0.01 to 0.1, across different true correlation structures, with 3 or 6 sequences, and with as few as 12 clusters
We have shown that GEE with small sample corrections are a robust method of analysis in a range of settings where SW-CRTs are commonly used
Summary
Many small sample corrections are available for generalised estimating equations[1] (GEE) with the sandwich standard-error estimator,[2,3,4,5,6,7] but there has been limited research on which are appropriate for use in stepped wedge trials (SW-CRTs) with a binary outcome
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