论文标题
将暴露不确定性纳入健康分析中的贝叶斯框架，并在空气污染和死产中应用
A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth
论文作者
论文摘要
对环境暴露与不利健康结果之间关系的研究通常取决于两阶段的统计建模方法，在该方法中，在第一阶段对暴露进行建模/预测，并用作在第二阶段单独拟合健康结果分析的输入。在估计关注的关联时，这些预测的不确定性经常被忽略或以过于简单的方式解释。我们在贝叶斯环境中工作，我们提出了一种灵活的内核密度估计（KDE）方法，用于从第一阶段建模/预测中充分利用后验输出，以准确地推断第二阶段的暴露与健康之间的关联，从而得出了有效拟合的有效模型所需的完整条件分布，并详细介绍了其与现有方法的连接，并通过模拟进行了模拟，并通过模拟进行了比较。我们的KDE方法通常显示出在几种设置和模型比较指标上的性能提高。使用竞争方法，我们调查了新泽西州（2011- 2015年）滞后的每日环境细颗粒物水平与死胎的关联，观察到在分娩前三天暴露的风险增加。新开发的方法可在r kdexp kdexp中获得。
Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner, when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure three days prior to delivery. The newly developed methods are available in the R package KDExp.
