Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures
Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures
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DOI：
10.1093/biostatistics/kxv048
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发表时间：
2016-04-01
期刊：
影响因子：
2.1
通讯作者：
Coull, Brent
中科院分区：
文献类型：
--
作者：
Alexeeff, Stacey E.;Carroll, Raymond J.;Coull, Brent
空气污染暴露的空间建模在空气污染流行病学研究中广泛应用，作为改进暴露评估的一种方法。然而，在对空气污染进行建模时，存在暴露模型不确定性的关键来源，包括估计误差和模型错误指定。我们研究了测量误差框架下线性健康影响模型中预测空气污染水平的使用。对于空气污染暴露的预测，我们考虑通用克里金框架，其中可能包括均值函数中的土地利用回归项和残差的空间协方差结构。我们推导了暴露模型中估计误差和模型错误指定引起的偏差，我们发现错误指定的暴露模型会在空气污染对健康的影响估计中引起渐近偏差。我们提出了一种新的空间模拟外推（SIMEX）程序，并证明该程序在纠正这种渐近偏差方面具有良好的性能。我们在马萨诸塞州空气污染和出生体重的研究中说明了空间 SIMEX。
Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts.