A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.
A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.
复制标题
将暴露不确定性纳入健康分析并应用于空气污染和死产的贝叶斯框架。
DOI：
10.1093/biostatistics/kxac034
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Warren,JoshuaL
中科院分区：
文献类型：
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作者：
Comess,Saskia;Chang,HowardH;Warren,JoshuaL
对环境暴露与不良健康结果之间关系的研究通常依赖于两阶段统计建模方法，其中在第一阶段对暴露进行建模/预测，并用作第二阶段单独拟合的健康结果分析的输入。在估计感兴趣的关联时，这些预测的不确定性经常被忽略，或以过于简单的方式进行解释。在贝叶斯环境下，我们提出了一种灵活的核密度估计（KDE）方法，充分利用第一阶段建模/预测的后验输出，对第二阶段暴露与健康之间的关联进行准确推断，导出有效模型拟合所需的完整条件分布，详细说明其与现有方法的联系，并通过模拟比较其性能。事实证明，我们的 KDE 方法在多个设置和模型比较指标上总体上提高了性能。我们使用竞争性方法，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产数量之间的关联，观察到分娩前 3 天接触量增加的风险增加。新开发的方法可在 R 包 KDExp 中找到。
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 3 days prior to delivery. The newly developed methods are available in the R packageKDExp.