Schuler Megan S, Chu Wanghuan, Coffman Donna
Department of Health Care Policy, Harvard Medical School, Boston, MA 02215.
Google, Inc., Mountain View, CA 94043, USA.
Health Serv Outcomes Res Methodol. 2016 Dec;16(4):271-292. doi: 10.1007/s10742-016-0157-5. Epub 2016 Aug 25.
Propensity score methods (e.g., matching, weighting, subclassification) provide a statistical approach for balancing dissimilar exposure groups on baseline covariates. These methods were developed in the context of data with no hierarchical structure or clustering. Yet in many applications the data have a clustered structure that is of substantive importance, such as when individuals are nested within healthcare providers or within schools. Recent work has extended propensity score methods to a multilevel setting, primarily focusing on binary exposures. In this paper, we focus on propensity score weighting for a continuous, rather than binary, exposure in a multilevel setting. Using simulations, we compare several specifications of the propensity score: a random effects model, a fixed effects model, and a single-level model. Additionally, our simulations compare the performance of marginal versus cluster-mean stabilized propensity score weights. In our results, regression specifications that accounted for the multilevel structure reduced bias, particularly when cluster-level confounders were omitted. Furthermore, cluster mean weights outperformed marginal weights.
倾向得分方法（例如匹配、加权、亚分类）提供了一种统计方法，用于在基线协变量上平衡不同的暴露组。这些方法是在没有层次结构或聚类的数据背景下开发的。然而，在许多应用中，数据具有具有实质性重要性的聚类结构，例如当个体嵌套在医疗保健提供者或学校中时。最近的工作已将倾向得分方法扩展到多级设置，主要侧重于二元暴露。在本文中，我们关注多级设置中连续而非二元暴露的倾向得分加权。通过模拟，我们比较了倾向得分的几种规格：随机效应模型、固定效应模型和单级模型。此外，我们的模拟比较了边际倾向得分权重与聚类均值稳定倾向得分权重的性能。在我们的结果中，考虑多级结构的回归规格减少了偏差，特别是在省略聚类水平混杂因素时。此外，聚类均值权重优于边际权重。
