Controlling for confounding in the presence of measurement error in hierarchical models: a Bayesian approach.
Controlling for confounding in the presence of measurement error in hierarchical models: a Bayesian approach.
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控制分层模型中存在测量误差的混杂：贝叶斯方法。
DOI：
10.1038/sj.jes.7500624
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发表时间：
2007
影响因子：
4.5
通讯作者：
Schwartz,Joel
中科院分区：
文献类型：
--
作者：
Gryparis,Alexandros;Coull,BrentA;Schwartz,Joel
研究空气污染对健康影响的一个主要问题是，污染物浓度与健康结果之间的观察到的关联是否全部或部分归因于暴露与第二种污染物或混杂因素之间的相关性。将曝光测量误差添加到这样的数据使问题进一步复杂化。当数据来自多城市研究时，为了解释测量误差，Schwartz和Coull（2003）提出了一个两阶段估计。这些作者通过第一原理和模拟表明，他们的方法可以为感兴趣的参数提供无偏估计。然而，这些估计数有很大的可变性。在本文中，我们描述了一个完全贝叶斯方法，产生的估计，比现有的两阶段测量误差校正更有效，但仍然是无偏的。所提出的方法还可以包含额外的暴露或混杂因素，而不需要现有模型公式中所必需的严格假设。我们比较现有的方法，通过模拟的贝叶斯估计的属性。
A major concern in studies that address the health effects of air pollution is whether an observed association between concentrations of a pollutant and a health outcome is all, or in part, due to the correlation between that exposure and either a second pollutant or a confounder. The addition of exposure measurement error to such data complicates matters further. To account for measurement error when data come from a multi-city study, Schwartz and Coull (2003) proposed a two-stage estimator. These authors showed via both first principles and simulation that their approach yields unbiased estimates for the parameters of interest. However, these estimates have large variability. In this paper, we describe a fully Bayesian approach that yields estimators that are much more efficient than the existing two-stage measurement error correction yet still unbiased. The proposed approach can also incorporate additional exposures or confounders without requiring strict assumptions that are necessary in existing formulations of the model. We compare the properties of the Bayesian estimators to existing approaches via simulation.