Department of Statistical Science, Duke University, Box 90251, Durham, NC 27708, USA.
Stat Med. 2011 Jul 30;30(17):2187-98. doi: 10.1002/sim.4234. Epub 2011 May 17.
In environmental health studies air pollution measurements from the closest monitor are commonly used as a proxy for personal exposure. This technique assumes that air pollution concentrations are spatially homogeneous in the neighborhoods associated with the monitors and consequently introduces measurement error into a resultant model. To model the relationship between maternal exposure to air pollution and birth weight, we build a hierarchical model that accounts for the associated measurement error. We allow four possible scenarios, with increasing flexibility, for capturing this uncertainty. In the two simplest cases, we specify models with a constant variance term and a variance component that allows uncertainty in the exposure measurements to increase as the distance between maternal residence and the location of the closest monitor increases. In the remaining two models, we introduce spatial dependence using random effects. The models are illustrated using Bayesian hierarchical modeling techniques that relate pregnancy outcomes from the North Carolina Detailed Birth Records to air pollution data from the U.S. Environmental Protection Agency.
在环境健康研究中，通常使用最近监测器的空气污染测量值作为个人暴露的替代物。这种技术假设与监测器相关的社区中空气污染浓度在空间上是均匀的，因此会在得出的模型中引入测量误差。为了建立母体暴露于空气污染与出生体重之间的关系模型，我们构建了一个层次模型，该模型考虑了相关的测量误差。我们允许四种可能的情况，以越来越灵活的方式来捕捉这种不确定性。在两种最简单的情况下，我们指定了具有恒定方差项和方差分量的模型，允许随着母体居住地与最近监测器位置之间的距离增加，暴露测量值的不确定性增加。在其余两种模型中，我们使用随机效应引入空间相关性。我们使用贝叶斯层次建模技术来说明这些模型，该技术将北卡罗来纳州详细出生记录中的妊娠结果与美国环境保护署的空气污染数据相关联。
