Propensity scores using missingness pattern information: a practical guide.
Propensity scores using missingness pattern information: a practical guide.
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DOI：
10.1002/sim.8503
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发表时间：
2020-05-20
影响因子：
2
通讯作者：
Williamson EJ
中科院分区：
文献类型：
--
作者：
Blake HA;Leyrat C;Mansfield KE;Seaman S;Tomlinson LA;Carpenter J;Williamson EJ
电子健康记录是调查与健康有关的问题的宝贵数据来源，倾向得分分析已成为解决此类调查中令人困惑的偏见的日益流行的方法。然而，由于电子健康记录通常被记录为标准临床护理的一部分，因此经常缺少值，特别是对于潜在的混杂因素。在我们的激励性研究中-使用电子健康记录来调查肾素-血管紧张素系统阻滞剂对急性肾损伤风险的影响-两个关键混杂因素，种族和慢性肾脏疾病阶段，分别有59%和53%的数据缺失。缺失模式方法是缺失指示器方法的一种变体，已被提出作为一种处理倾向得分分析中部分观察到的混杂因素的方法。在MPA中，针对数据中存在的每种遗漏模式分别估计倾向分数。尽管在文献中陈述了作为MPA有效性基础的假设，但在实践中可能很难评估它们的合理性。在这篇文章中，我们通过使用因果关系图在一系列简单的情景中评估其合理性，对可能违反这些假设的情况得出一般性结论，从而探索MPA的基本假设。我们提出了一个框架，为评估MPA的假设在特定环境下是否可信，从而决定MPA何时是适当的提供了实用指导。我们将我们的框架应用到我们的激励性研究中，表明MPA的基本假设似乎是合理的，并展示了MPA在本研究中的应用。
Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In ourmotivating study—using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury—two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA’s underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessingwhether the MPA’s assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA’s underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.
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