Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials (CRTs) 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 (GEE) for assessing treatment effectiveness in stepped wedge CRTs. This approach has advantages over the more commonly used mixed models that () the population-average parameters have an important interpretation for public health applications and () they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore providing more robust evidence on treatment effects. However, CRTs typically have a small number of clusters, rendering the standard GEE sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic GEE inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to GEE for stepped wedge CRTs and for parallel CRTs 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 CRTs. 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.
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