Adjusting for confounding by neighborhood using generalized linear mixed models and complex survey data
Adjusting for confounding by neighborhood using generalized linear mixed models and complex survey data
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DOI：
10.1002/sim.5624
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发表时间：
2013-04-15
影响因子：
2
通讯作者：
Dailey, Amy B.
中科院分区：
文献类型：
--
作者：
Brumback, Babette A.;Zheng, Hao W.;Dailey, Amy B.
在调查健康差异时，探讨社区一级的社会经济因素调整是否可以解释甚至逆转未调整的差异可能是有意义的。最近，我们提出了新的方法来调整个人水平的协变量的影响，通过使用复杂的调查数据和条件似然方法的推广未测量的邻里水平的协变量的混杂。广义线性混合模型（GLMM）在许多情况下是条件似然方法的一种流行的替代方法。因此，在本文中，我们提出并研究了一种新的适应GLSTO复杂的调查数据，实现了相同的目标，调整未测量的邻域水平的协变量的混杂。与新的GLMM方法，必须正确地建模的预期的未测量的邻居水平的影响作为一个函数的个人水平的协变量。我们使用模拟证明，即使该模型是正确的，人口普查数据的个人水平的协变量有时需要一致的估计个人水平的协变量的影响。我们应用新的方法来调查牙齿清洁的近期差异，作为一个有序的结果，使用2008年佛罗里达行为风险因素监测系统（BRFSS）调查的数据。我们将社区操作为邮政编码，并将BRFSS数据与邮政编码列表区域的人口普查数据合并，以将人口普查数据纳入个人水平的协变量。我们将新的结果与我们以前使用条件似然方法的分析进行比较。我们发现，结果是定性相似的。版权所有（c）2012约翰威利父子有限公司
When investigating health disparities, it can be of interest to explore whether adjustment for socioeconomic factors at the neighborhood level can account for, or even reverse, an unadjusted difference. Recently, we proposed new methods to adjust the effect of an individual-level covariate for confounding by unmeasured neighborhood-level covariates using complex survey data and a generalization of conditional likelihood methods. Generalized linear mixed models (GLMMs) are a popular alternative to conditional likelihood methods in many circumstances. Therefore, in the present article, we propose and investigate a new adaptation of GLMMs for complex survey data that achieves the same goal of adjusting for confounding by unmeasured neighborhood-level covariates. With the new GLMM approach, one must correctly model the expectation of the unmeasured neighborhood-level effect as a function of the individual-level covariates. We demonstrate using simulations that even if that model is correct, census data on the individual-level covariates are sometimes required for consistent estimation of the effect of the individual-level covariate. We apply the new methods to investigate disparities in recency of dental cleaning, treated as an ordinal outcome, using data from the 2008 Florida Behavioral Risk Factor Surveillance System (BRFSS) survey. We operationalize neighborhood as zip code and merge the BRFSS data with census data on ZIP Code Tabulated Areas to incorporate census data on the individual-level covariates. We compare the new results to our previous analysis, which used conditional likelihood methods. We find that the results are qualitatively similar. Copyright (c) 2012 John Wiley & Sons, Ltd.