论文标题
利用基线协变量来分析具有罕见二元结果的小聚类随机试验
Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome
论文作者
论文摘要
聚类随机试验（CRT）涉及将整个参与者（称为簇）随机进行治疗组，但通常由有限或固定数量的可用簇组成。虽然协变量调整可以解释治疗组之间的偶然失衡并提高单独随机试验的统计效率，但小型CRT中单个级别协变量调整的分析方法迄今为止很少关注。在本文中，我们通过广泛的模拟系统地调查了倾向评分加权和多变量回归的工作特性，作为两个个人级别的协变量调整策略，用于估计具有罕见的二元结果的小型CRT中参与者平均因果关系的效果，并确定每个调整策略在每个方案中都具有相对效率的优势，而不是在其他方面获得相对效率的优势来实践推荐。我们还研究了与倾向评分权重和多变量回归相关的偏置校正三明治方差估计量的有限样本性能，以量化估计参与者平均治疗效果的不确定性。为了说明个人级别协变量调整的方法，我们重新分析了最近在31个小儿重症监护病房中测试镇静协议的CRT。
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 paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units.
