Covariate-constrained randomization with cluster selection and substitution.

ABSTRACT:

Background

An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four specified covariates and geographic sector. Each cluster is within a census tract of the city of Mérida, Mexico, and there were 133 eligible tracts from which to select 50. As some selected clusters may have been subsequently found unsuitable in the field, we desired a strategy to substitute new clusters while maintaining covariate balance.

Methods

We developed an algorithm that successfully identified a subset of clusters that maximized the average minimum pairwise distance between clusters in order to reduce contamination and balanced the specified covariates both before and after substitutions were m

SUBMITTER: Crisp AM 

PROVIDER: S-EPMC10257748 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature