Improved semiparametric time series models of air pollution and mortality
Improved semiparametric time series models of air pollution and mortality
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DOI：
10.1198/016214504000000656
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发表时间：
2004-12-01
影响因子：
3.7
通讯作者：
Hastie, TJ
中科院分区：
文献类型：
--
作者：
Dominici, F;McDermott, A;Hastie, TJ
2002年，围绕空气污染和健康的时间序列分析的方法问题吸引了科学界、决策者、新闻界和与空气污染有关的各种利益攸关方的注意。随着美国环境保护署（EPA）对颗粒物空气污染（PM）的流行病学证据的最新审查的完成，统计学家和流行病学家发现，广义加性模型（GAM）的S-PLUS实施可能会高估空气污染的影响，并低估空气污染和健康时间序列研究中的统计不确定性。这一发现推迟了作为美国国家环境空气质量标准审查一部分的PM标准文件的完成，因为时间序列结果代表了证据的关键组成部分。此外，委员会还对目前的模型表述及其软件实施的适当性表示关切。在这篇文章中，我们提供了改进的半参数回归直接相关的空气污染的时间序列研究中的风险估计。首先，我们介绍了一个封闭的形式估计的渐近准确的协方差矩阵的线性分量的GAM。为了简化这些计算的实现，我们开发了S包gam.exact，一个扩展版本的增益。使用gam.exact可以更可靠地评估估计污染系数的统计不确定性。其次，我们开发了一种带宽选择方法，以减少由于不可测量的随时间变化的因素，如季节和流感流行的污染-死亡率关系的混淆偏差。第三，我们引入了一个概念框架，以充分探讨空气污染风险估计模型选择的敏感性。我们将我们的方法应用于全国死亡率和空气污染研究的数据，其中包括1987-1994年期间美国90个最大城市的时间序列数据。
In 2002, methodological issues around time series analyses of air pollution and health attracted the attention of the scientific community, policy makers, the press, and the diverse stakeholders concerned with air pollution. As the U.S. Environmental Protection Agency (EPA) was finalizing its most recent review of epidemiologic evidence on particulate matter air pollution (PM), statisticians and epidemiologists found that the S-PLUS implementation of generalized additive models (GAMs) can overestimate effects of air pollution and understate statistical uncertainty in time series studies of air pollution and health. This discovery delayed completion of the PM Criteria Document prepared as part of the review of the U.S. National Ambient Air Quality Standard, because the time series findings represented a critical component of the evidence. In addition, it raised concerns about the adequacy of current model formulations and their software implementations. In this article we provide improvements in semiparametric regression directly relevant to risk estimation in time series studies of air pollution. First, we introduce a closed-form estimate of the asymptotically exact covariance matrix of the linear component of a GAM. To ease the implementation of these calculations, we develop the S package gam.exact, an extended version of gain. Use of gam.exact allows a more robust assessment of the statistical uncertainty of the estimated pollution coefficients. Second, we develop a bandwidth selection method to reduce confounding bias in the pollution-mortality relationship due to unmeasured time-varying factors, such as season and influenza epidemics. Third, we introduce a conceptual framework to fully explore the sensitivity of the air pollution risk estimates to model choice. We apply our methods to data of the National Mortality Morbidity Air Pollution Study, which includes time series data from the 90 largest U.S. cities for the period 1987-1994.