Robust estimation of the causal effect of time-varying neighborhood factors on health outcomes.
Robust estimation of the causal effect of time-varying neighborhood factors on health outcomes.
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DOI：
10.1002/sim.8423
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发表时间：
2020-02-28
影响因子：
2
通讯作者：
Slaughter, Mary Ellen
中科院分区：
文献类型：
--
作者：
Robbins, Michael W.;Griffin, Beth Ann;Shih, Regina A.;Slaughter, Mary Ellen
关键词：
在观测数据中建立暴露和结果之间的因果关系的根本困难涉及从混杂因素中分离因果关系。这个问题是许多社区研究的基础，这些研究充斥着在纵向数据中考虑社区特征和健康结果之间关系的研究。这样的分析被选择问题搞混了；健康结果(或相关特征)高于平均水平的个人可能会自我选择进入有利的社区。通常用于评估观察性纵向数据中的因果推断的技术，如逆治疗概率加权(IPTW)，由于此类数据的独特特性，可能不适合用于邻里数据。通过引入一种更适合邻域数据的基于多元核密度函数的方法，我们改进了IPTW工具包。将所提出的加权方法与边缘构造模型结合应用。我们的实证分析使用了来自健康和退休研究的纵向数据；我们的兴趣暴露是社区社会经济状况(NSE)的一个指数，我们检查了它对认知功能的影响。我们的发现说明了选择IPTW方法的重要性--比较加权方法在协变量集上提供了较差的平衡(这不是我们首选的程序的情况)，并在应用于结果模型时产生误导性的结果。通过仿真验证了多元核函数的有效性。此外，我们的研究结果强调了IPTW的重要性--在没有IPTW的回归中控制协变量表明NSE影响认知，而IPTW加权模型没有显示出统计上的显著影响。
The fundamental difficulty of establishing causal relationships between an exposure and an outcome in observational data involves disentangling causality from confounding factors. This problem underlies much of neighborhoods research, which abounds with studies that consider associations between neighborhood characteristics and health outcomes in longitudinal data. Such analyses are confounded by selection issues; individuals with above average health outcomes (or associated characteristics) may self-select into advantaged neighborhoods. Techniques commonly used to assess causal inferences in observational longitudinal data, such as inverse probability of treatment weighting (IPTW), may be inappropriate in neighborhoods data due to unique characteristics of such data. We advance the IPTW toolkit by introducing a procedure based on a multivariate kernel density function which is more appropriate for neighborhoods data. The proposed weighting method is applied in conjunction with a marginal structural model. Our empirical analyses use longitudinal data from the Health and Retirement Study; our exposure of interest is an index of neighborhood socioeconomic status (NSES), and we examine its influence on cognitive function. Our findings illustrate the importance of the choice of method for IPTW—the comparison weighting methods provide poor balance across the set of covariates (which is not the case for our preferred procedure) and yield misleading results when applied in the outcomes models. The utility of the multivariate kernel is also validated via simulation. In addition, our findings emphasize the importance of IPTW—controlling for covariates within a regression without IPTW indicates that NSES affects cognition, whereas IPTW-weighted models fail to show a statistically significant effect.
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影响因子：
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作者：
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