论文标题
MatchThem ::多次插补后的匹配和加权
MatchThem:: Matching and Weighting after Multiple Imputation
论文作者
论文摘要
通过匹配或加权，在观察性研究中，在估计暴露与结果之间的关联并降低对某些建模假设的依赖程度时，通过匹配或加权来平衡混杂因素在观察性研究中的分布是控制混淆的一种公认方法。尽管实践中越来越受欢迎，但这些过程不能立即应用于具有缺失值的数据集。丢失数据的多个插补是一种流行的方法，可以在保留数据集中的单元数量并考虑缺失值中的不确定性时说明缺失值。但是，据我们所知，没有全面的匹配和加权软件可以轻松地使用多重估算的数据集实现。在本文中，我们回顾了此问题，并提出了一个框架，以将匹配和加权乘以估算的数据集绘制为5个操作，以及在匹配和加权后评估这些数据集平衡的最佳实践。我们还使用r，matchthem的伴侣软件包说明了这些方法。
Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and to reduce the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, to the best of our knowledge, there is no comprehensive matching and weighting software that can be easily implemented with multiply imputed datasets. In this paper, we review this problem and suggest a framework to map out the matching and weighting multiply imputed datasets to 5 actions as well as the best practices to assess balance in these datasets after matching and weighting. We also illustrate these approaches using a companion package for R, MatchThem.
