Abstract

Propensity score weighting and outcome regression are popular ways to adjust for observed confounders in epidemiologic research. Here, we provide an introduction to matching methods, which serve the same purpose but can offer advantages in robustness and performance. A key difference between matching and weighting methods is that matching methods do not directly rely on the propensity score and so are less sensitive to its misspecification or to the presence of extreme values. Matching methods offer many options for customization, which allow a researcher to incorporate substantive knowledge and carefully manage bias/variance trade-offs in estimating the effects of nonrandomized exposures. We review these options and their implications, provide guidance for their use, and compare matching methods with weighting methods. Because of their potential advantages over other methods, matching methods should have their place in an epidemiologist’s methodological toolbox.

中文翻译：

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混杂因素调整的匹配方法：流行病学家工具箱的补充

摘要

倾向得分加权和结果回归是调整流行病学研究中观察到的混杂因素的流行方法。在这里，我们介绍了匹配方法，它们具有相同的目的，但在鲁棒性和性能方面具有优势。匹配方法和加权方法之间的一个关键区别在于，匹配方法不直接依赖于倾向得分，因此对其错误指定或极值的存在不太敏感。匹配方法为定制提供了许多选项，允许研究人员在估计非随机暴露的影响时结合实质性知识并仔细管理偏差/方差权衡。我们回顾了这些选项及其含义，为其使用提供指导，并将匹配方法与加权方法进行比较。