Abstract:Cluster‐based outcome‐dependent sampling (ODS) has the potential to yield efficiency gains when the outcome of interest is relatively rare, and resource constraints allow only a certain number of clusters to be visited for data collection. Previous research has shown that when the intended analysis is inverse‐probability weighted generalized estimating equations, and the number of clusters that can be sampled is fixed, optimal allocation of the (cluster‐level) sample size across strata defined by auxiliary var… Show more
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