Measurement error caused by spatial misalignment in environmental epidemiology
Measurement error caused by spatial misalignment in environmental epidemiology
复制标题
DOI：
10.1093/biostatistics/kxn033
复制
发表时间：
2009-04-01
期刊：
影响因子：
2.1
通讯作者：
Coull, Brent A.
中科院分区：
文献类型：
--
作者：
Gryparis, Alexandros;Paciorek, Christopher J.;Coull, Brent A.
在许多环境流行病学研究中，接触测量和健康评估的地点和/或时间并不匹配。在这种情况下，健康影响分析通常使用暴露模型的预测作为回归模型中的协变量。这种曝光预测包含一些测量误差，因为预测值不等于真实曝光。我们提供了一个框架的空间测量误差建模，平滑引起的Berkson型测量误差与非对角误差结构。从这个角度来看，我们回顾了现有的方法来估计线性回归健康模型，包括直接使用的空间预测和暴露模拟，并探讨了一些修改的方法，包括贝叶斯模型和样本外回归校准，测量误差原则的动机。然后，我们将这项工作扩展到健康结果的广义线性模型框架。基于分析考虑和模拟结果，我们比较了所有这些方法的性能在几个空间模型的曝光。我们的比较强调了几个重要的观点。首先，曝光模拟在某些现实场景下可能表现得很差。其次，不同方法的相对性能取决于底层暴露表面的性质。第三，传统的测量误差概念有助于解释不同方法的相对实际性能。我们将这些方法应用于大波士顿地区颗粒物水平与出生体重之间的关联数据。
In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.