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Abstract:
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Stepped wedge designs (SWD) randomize clusters to the time period during which newly enrolled patients will receive the intervention under study in a sequential rollout over time. By the study's end, patients enrolling at all clusters will receive the intervention, eliminating ethical concerns related to withholding efficacious treatments. Little statistical theory for these designs exists for binary outcomes. To address this, we utilized a maximum likelihood approach and developed numerical methods to determine the asymptotic power of the SWD. We studied how power of a SWD for detecting risk differences varies as a function of the number of clusters, cluster size, the baseline risk, the intervention effect, and the intra-cluster correlation coefficient. We studied the robustness of power to the assumed form of the distribution of the cluster random effects, as well as how power is affected by variable cluster size. SWD power is sensitive to neither, in contrast to the parallel cluster randomized design which is highly sensitive to variable cluster size.
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