Practical strategies for operationalizing optimal allocation in stratified cluster-based outcome-dependent sampling designs.

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 variables readily available at the design stage has the potential to increase efficiency in the estimation of the parameter(s) of interest. In such a setting, the optimal allocation formulae depend on quantities that are unknown in practice, currently making such designs difficult to imp

SUBMITTER: Sauer S 

PROVIDER: S-EPMC10006324 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature