Estimating Population Treatment Effects From a Survey Subsample
Estimating Population Treatment Effects From a Survey Subsample
复制标题
DOI：
10.1093/aje/kwu197
复制
发表时间：
2014-10-01
影响因子：
5
通讯作者：
Stuart, Elizabeth A.
中科院分区：
文献类型：
--
作者：
Rudolph, Kara E.;Diaz, Ivan;Stuart, Elizabeth A.
我们考虑了使用调查子样本估计目标人群的平均治疗效果的问题。我们的动机是推广的治疗效果，估计在一个子样本的全国科摩罗调查复制青少年补充（2001-2004年）的美国青少年人口。为了解决这个问题，我们评估了易于实现的方法，占非随机治疗分配和非随机2阶段选择机制。我们比较了使用逆概率加权的Horvitz-Thompson估计器和2个双稳健估计器在各种情况下的性能。我们证明了，2双稳健估计一般优于逆概率加权的均方误差，即使在错误的治疗，选择，或结果模型之一。此外，双稳健估计易于实现，并为应用流行病学研究人员提供了一个有吸引力的替代逆概率加权。我们演示了如何将这些估计器应用于激励示例。
We considered the problem of estimating an average treatment effect for a target population using a survey subsample. Our motivation was to generalize a treatment effect that was estimated in a subsample of the National Comorbidity Survey Replication Adolescent Supplement (2001-2004) to the population of US adolescents. To address this problem, we evaluated easy-to-implement methods that account for both nonrandom treatment assignment and a nonrandom 2-stage selection mechanism. We compared the performance of a Horvitz-Thompson estimator using inverse probability weighting and 2 doubly robust estimators in a variety of scenarios. We demonstrated that the 2 doubly robust estimators generally outperformed inverse probability weighting in terms of mean-squared error even under misspecification of one of the treatment, selection, or outcome models. Moreover, the doubly robust estimators are easy to implement and provide an attractive alternative to inverse probability weighting for applied epidemiologic researchers. We demonstrated how to apply these estimators to our motivating example.