Cluster randomised trials with a binary outcome and a small number of clusters: comparison of individual and cluster level analysis method.

ABSTRACT:

Background

Cluster randomised trials (CRTs) are often designed with a small number of clusters, but it is not clear which analysis methods are optimal when the outcome is binary. This simulation study aimed to determine (i) whether cluster-level analysis (CL), generalised linear mixed models (GLMM), and generalised estimating equations with sandwich variance (GEE) approaches maintain acceptable type-one error including the impact of non-normality of cluster effects and low prevalence, and if so (ii) which methods have the greatest power. We simulated CRTs with 8-30 clusters, altering the cluster-size, outcome prevalence, intracluster correlation coefficient, and cluster effect distribution. We analysed each dataset with weighted and unweighted CL; GLMM with adaptive quadrature and

SUBMITTER: Thompson JA 

PROVIDER: S-EPMC9375419 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature