Choi Jungsoon, Lawson Andrew B
1 Department of Mathematics, College of Natural Sciences, Hanyang University, Seoul, South Korea.
2 Research Institute for Natural Sciences, Hanyang University, Seoul, South Korea.
Stat Methods Med Res. 2019 Sep;28(9):2570-2582. doi: 10.1177/0962280218767980. Epub 2018 Apr 11.
In space-time epidemiological modeling, most studies have considered the overall variations in relative risk to better estimate the effects of risk factors on health outcomes. However, the associations between risk factors and health outcomes may vary across space and time. Especially, the temporal patterns of the covariate effects may depend on space. Thus, we propose a Bayesian two-stage spatially dependent variable selection approach for space-time health data to determine the spatially varying subsets of regression coefficients with common temporal dependence. The two-stage structure allows reduction of the spatial confounding bias in the estimates of the regression coefficients. A simulation study is conducted to examine the performance of the proposed two-stage model. We apply the proposed model to the number of inpatients with lung cancer in 159 counties of Georgia, USA.
在时空流行病学建模中，大多数研究考虑了相对风险的总体变化，以便更好地估计风险因素对健康结果的影响。然而，风险因素与健康结果之间的关联可能随空间和时间而变化。特别是，协变量效应的时间模式可能取决于空间。因此，我们针对时空健康数据提出了一种贝叶斯两阶段空间依赖变量选择方法，以确定具有共同时间依赖性的回归系数的空间变化子集。两阶段结构能够减少回归系数估计中的空间混杂偏差。进行了一项模拟研究以检验所提出的两阶段模型的性能。我们将所提出的模型应用于美国佐治亚州159个县的肺癌住院患者数量。
