Handling missing data in partially clustered randomized controlled trials.

ABSTRACT: Partially clustered designs are widely used in psychological research, especially in randomized controlled trials that examine the effectiveness of prevention or intervention strategies. In a partially clustered trial, individuals are clustered into intervention groups in one or more study arms, for the purpose of intervention delivery, whereas individuals in other arms (e.g., the waitlist control arm) are unclustered. Missing data are almost inevitable in partially clustered trials and could pose a major challenge in drawing valid research conclusions. This article focuses on handling auxiliary-variable-dependent missing at random data in partially clustered studies. Five methods were compared via a simulation study, including simultaneous multiple imputation using joint modeling (MI-JM-S

SUBMITTER: Yang M 

PROVIDER: S-EPMC11906213 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature