小区域健康数据中暴露存在空间错位情况下的贝叶斯两阶段时空混合建模
Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data.
作者信息
Lawson Andrew B, Choi Jungsoon, Cai Bo, Hossain Monir, Kirby Russell S, Liu Jihong
机构信息
Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina, Charleston, SC 29403, USA.
Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
出版信息
J Agric Biol Environ Stat. 2012 Sep;17(3):417-441. doi: 10.1007/s13253-012-0100-3. Epub 2012 Aug 9.
Abstract
We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008.
摘要
我们开发了一种新的贝叶斯两阶段时空混合模型，以研究空气污染对哮喘的影响。所提出的两阶段混合模型能够识别时间上的潜在结构，并估计协变量对健康结果的影响。在本文中，我们还考虑了暴露数据与健康数据的空间错位问题。我们进行了一项模拟研究，以评估两阶段混合模型的性能。我们将我们的统计框架应用于美国佐治亚州1999 - 2008年县级门诊哮喘数据集。
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