Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial

作者：Brennan C Kahan, Fan Li, Bryan S. Blette, Vipul Jairath, Andrew Copas, Michael O. Harhay · 发表于：Clinical Trials · 年份：2023 · DOI：10.1177/17407745231186094 · 被引用次数：22 · 研究领域：Statistical Methods and Bayesian Inference、Meta-analysis and systematic reviews、Statistical Methods in Clinical Trials

BACKGROUND: Recent work has shown that cluster-randomised trials can estimate two distinct estimands: the participant-average and cluster-average treatment effects. These can differ when participant outcomes or the treatment effect depends on the cluster size (termed informative cluster size). In this case, estimators that target one estimand (such as the analysis of unweighted cluster-level summaries, which targets the cluster-average effect) may be biased for the other. Furthermore, commonly used estimators such as mixed-effects models or generalised estimating equations with an exchangeable correlation structure can be biased for both estimands. However, there has been little empirical research into whether informative cluster size is likely to occur in practice. METHOD: We re-analysed a cluster-randomised trial comparing two different thresholds for red blood cell transfusion in patients with acute upper gastrointestinal bleeding to explore whether estimates for the participant- and cluster-average effects differed, to provide empirical evidence for whether informative cluster size may be present. For each outcome, we first estimated a participant-average effect using independence estimating equations, which are unbiased under informative cluster size. We then compared this to two further methods: (1) a cluster-average effect estimated using either weighted independence estimating equations or unweighted cluster-level summaries, and (2) estimates from a mixed-effects mode...