Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome
利用基线协变量分析罕见二元结局的小型整群随机试验
Cluster-randomized trials (CRTs) involve randomizing entire groups of participants -- called clusters -- to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually-randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this…