Standard sample size calculation formulas for Stepped Wedge Cluster Randomized Trials (SW-CRTs) assume that cluster sizes are equal. When cluster sizes vary substantially, ignoring this variation may lead to an under-powered study. We investigate the relative efficiency of a SW-CRT with varying cluster sizes to equal cluster sizes, and derive variance estimators for the intervention effect that account for this variation under the assumption of a mixed effects model; a commonly-used approach for analyzing data from cluster randomized trials. When cluster sizes vary, the power of a SW-CRT depends on the order in which clusters receive the intervention, which is determined through randomization. We first derive a variance formula that corresponds to any particular realization of the randomized sequence and propose efficient algorithms to identify upper and lower bounds of the power. We then obtain an ``expected'' power based on a first-order approximation to the variance formula, where the expectation is taken with respect to all possible randomization sequences. Finally, we provide a variance formula for more general settings where only the mean and coefficient of variation of cluster sizes, instead of exact cluster sizes, are known in the design stage. We evaluate our methods through simulations and illustrate that the power of a SW-CRT decreases as the variation in cluster sizes increases, and the impact is largest when the number of clusters is small.



Included in

Biostatistics Commons