Propensity Score Analysis with Survey Weighted Data.
Propensity Score Analysis with Survey Weighted Data.
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DOI：
10.1515/jci-2014-0039
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发表时间：
2015-09
影响因子：
1.4
通讯作者：
Kabeto MU
中科院分区：
文献类型：
--
作者：
Ridgeway G;Kovalchik SA;Griffin BA;Kabeto MU
倾向评分分析（PSA）是估计治疗效果的常用方法，但处理调查设计数据的研究人员通常没有正确解释他们分析中的抽样权重。此外，关于这一主题的现有方法学文章中提出的建议容易产生偏见。我们通过推导、模拟和一个真实的数据例子表明，在倾向评分估计阶段和结果模型阶段使用抽样权重，可以得到一个对各种条件具有鲁棒性的估计量，这些条件会导致统计文献中目前推荐的估计量出现偏倚。我们强烈建议研究人员使用这里描述的更强大的方法。这篇文章为研究人员提供了非常需要的严格的统计指导，涉及抽样权重和使用PSA的调查设计。
Propensity score analysis (PSA) is a common method for estimating treatment effects, but researchers dealing with data from survey designs are generally not properly accounting for the sampling weights in their analyses. Moreover, recommendations given in the few existing methodological articles on this subject are susceptible to bias. We show in this article through derivation, simulation, and a real data example that using sampling weights in the propensity score estimation stage and the outcome model stage results in an estimator that is robust to a variety of conditions that lead to bias for estimators currently recommended in the statistical literature. We highly recommend researchers use the more robust approach described here. This article provides much needed rigorous statistical guidance for researchers working with survey designs involving sampling weights and using PSAs.