Department of Statistics, Colorado State University, Fort Collins, CO, United states of America.
Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, United states of America.
PLoS One. 2021 Mar 25;16(3):e0249236. doi: 10.1371/journal.pone.0249236. eCollection 2021.
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.
在研究与化学混合物相关的健康影响时会面临挑战。最近提出了几种方法来估计健康结果与接触化学混合物之间的关联，但缺乏对广泛方法的正式模拟研究。我们选择了五种最近开发的方法，并在模拟研究中评估了它们在估计暴露-反应函数、识别有效混合物成分和识别相互作用方面的性能。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。在估计暴露-反应函数和识别有效混合物成分方面，BKMR 和 NPB 优于其他当代方法和传统线性模型。在对哮喘儿童多污染物暴露对肺功能影响的数据分析中，BKMR 和 NPB 产生了相似的结果。
