论文标题
将推断从群集随机试验扩展到目标人群
Extending inferences from a cluster randomized trial to a target population
论文作者
论文摘要
我们描述了在一般的非参数模型下，将因果关系从聚类随机试验扩展到目标群的方法（概括或运输）的方法，该模型允许任意群集内依赖性。我们建议在目标人群中对潜在结果的强大估计量，以利用有关协变量和结果的个人级别数据以提高效率，并且适合与机器学习方法一起使用。我们使用了在嵌套在4,475个符合试验资格的Medicare认证的疗养院中的818家疗养院中进行的流感疫苗接种策略的聚类随机试验进行了说明。
We describe methods that extend (generalize or transport) causal inferences from cluster randomized trials to a target population of clusters, under a general nonparametric model that allows for arbitrary within-cluster dependence. We propose doubly robust estimators of potential outcome means in the target population that exploit individual-level data on covariates and outcomes to improve efficiency and are appropriate for use with machine learning methods. We illustrate the methods using a cluster randomized trial of influenza vaccination strategies conducted in 818 nursing homes nested in a cohort of 4,475 trial-eligible Medicare-certified nursing homes.
