Time-varying coefficient models for the analysis of air pollution and health outcome data
Time-varying coefficient models for the analysis of air pollution and health outcome data
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DOI：
10.1111/j.1541-0420.2007.00776.x
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发表时间：
2007-12-01
期刊：
影响因子：
1.9
通讯作者：
Shaddick, Gavin
中科院分区：
文献类型：
--
作者：
Lee, Duncan;Shaddick, Gavin
在这篇文章中，建立了一个时变系数模型来检验有害健康和短期(急性)空气污染暴露之间的关系。这一模型允许相对风险随时间演变，这可能是由于与温度的相互作用，或由于污染物组成的变化，如颗粒物，随着时间的推移。该模型产生了这些时变效应的平滑估计，不受研究者设置的固定参数形式的约束。取而代之的是，使用受惩罚的自然三次样条线从数据中估计形状。用拟似然和贝叶斯技术建立了泊松回归模型，分别用迭代加权最小二乘法和马尔可夫链蒙特卡罗模拟进行了估计。通过模拟研究评估了估计不同类型时变影响的方法的有效性，然后将这些模型应用于来自四个城市的数据，这些城市是全国发病率、死亡率和空气污染研究的一部分。
In this article a time-varying coefficient model is developed to examine the relationship between adverse health and short-term (acute) exposure to air pollution. This model allows the relative risk to evolve over time, which may be due to an interaction with temperature, or from a change in the composition of pollutants, such as particulate matter, over time. The model produces a smooth estimate of these time-varying effects, which are not constrained to follow a fixed parametric form set by the investigator. Instead, the shape is estimated from the data using penalized natural cubic splines. Poisson regression models, using both quasi-likelihood and Bayesian techniques, are developed, with estimation performed using an iteratively re-weighted least squares procedure and Markov chain Monte Carlo simulation, respectively. The efficacy of the methods to estimate different types of time-varying effects are assessed via a simulation study, and the models are then applied to data from four cities that were part of the National Morbidity, Mortality, and Air Pollution Study.