Gryparis Alexandros, Coull Brent A, Schwartz Joel
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, USA.
J Expo Sci Environ Epidemiol. 2007 Dec;17 Suppl 2:S20-8. doi: 10.1038/sj.jes.7500624.
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.
在探讨空气污染对健康影响的研究中，一个主要问题是，观察到的污染物浓度与健康结果之间的关联，是否全部或部分归因于该暴露与另一种污染物或混杂因素之间的相关性。此类数据中加入暴露测量误差会使情况更加复杂。为了在数据来自多城市研究时考虑测量误差，施瓦茨和库尔（2003年）提出了一种两阶段估计器。这些作者通过第一原理和模拟表明，他们的方法能够得到感兴趣参数的无偏估计。然而，这些估计的变异性很大。在本文中，我们描述了一种完全贝叶斯方法，该方法产生的估计器比现有的两阶段测量误差校正方法更有效，但仍然是无偏的。所提出的方法还可以纳入额外的暴露因素或混杂因素，而无需现有模型公式中所必需的严格假设。我们通过模拟比较了贝叶斯估计器与现有方法的性质。
