Estimating marginal treatment effect in cluster randomized trials with multi-level missing outcomes.

ABSTRACT: Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighing the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes. In the presence of missing clusters, for example, all outcomes from a cluster are missing due to drop-out of the cluster, these approaches ignore this cluster-level missingness and can lead to biased inference if the cluster-level missingness is informative. Informative missingness at multiple levels can also occur in CRTs with

SUBMITTER: Chang CR 

PROVIDER: S-EPMC11629964 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature