Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study.

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study.
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DOI:
10.1371/journal.pone.0249236
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发表时间:
2021
期刊:
影响因子:
3.7
通讯作者:
Wilson A
Wilson A
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Hoskovec L;Benka-Coker W;Severson R;Magzamen S;Wilson A

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在研究与化学混合物相关的健康影响方面出现了挑战。最近提出了几种方法来估计健康结果和接触化学混合物之间的关联,但缺乏一个正式的模拟研究比较广泛的方法。我们选择了五个最近开发的方法,并评估其性能估计的安全响应函数,确定活性混合物成分,并确定在模拟研究中的相互作用。贝叶斯核机器回归(BKMR)和非参数贝叶斯收缩(NPB)是我们模拟研究中表现最好的方法。BKMR和NPB在估计响应函数和识别活性混合物组分方面优于其他当代方法和传统线性模型。BKMR和NPB在多污染物暴露对哮喘儿童肺功能影响的数据分析中得出了相似的结果。
Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.
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