The toxicological evaluation of realistic emissions of source aerosols study: statistical methods.
The toxicological evaluation of realistic emissions of source aerosols study: statistical methods.
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DOI：
10.3109/08958378.2010.566291
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发表时间：
2011-08
影响因子：
2.1
通讯作者：
Godleski JJ
中科院分区：
文献类型：
--
作者：
Coull BA;Wellenius GA;Gonzalez-Flecha B;Diaz E;Koutrakis P;Godleski JJ
源气溶胶实际排放毒理学评估 (TERESA) 研究涉及三个燃煤电厂排放物的回收、老化和大气转化。对暴露于不同排放场景的大鼠进行毒理学评估，并具有广泛的暴露特征。生成的数据具有多个分辨率级别：暴露、场景和化学成分。在这里，我们概述了一种多层方法来分析暴露与健康影响之间的关联，从将暴露视为分类变量的标准方差分析模型开始。该模型评估了不同情景（按植物）暴露影响的差异。为了评估污染物浓度与健康之间未经调整的关联，使用暴露和控制条件下的响应平均值之间的差异以及作为预测因子的单一成分浓度进行单变量分析。然后，基于随机森林使用了一种新颖的暴露成分和健康多变量分析，随机森林是分类和回归树的最新扩展，应用于结果差异。对于每种暴露成分，这种方法产生了该成分在预测给定日期反应差异方面的重要性的非参数测量，并控制了模型中其他测量的成分浓度。最后，R2 分析比较了暴露场景、植物和成分浓度对每个结果的相对重要性。峰值呼气流量用于演示多个分析水平如何相互补充，以评估与健康影响最密切相关的成分。
The Toxicological Evaluation of Realistic Emissions of Source Aerosols (TERESA) study involved withdrawal, aging, and atmospheric transformation of emissions of three coal-fired power plants. Toxicological evaluations were carried out in rats exposed to different emission scenarios with extensive exposure characterization. Data generated had multiple levels of resolution: exposure, scenario and constituent chemical composition. Here, we outline a multilayered approach to analyze the associations between exposure and health effects beginning with standard ANOVA models that treat exposure as a categorical variable. The model assessed differences in exposure effects across scenarios (by plant). To assess unadjusted associations between pollutant concentrations and health, univariate analyses were conducted using the difference between the response means under exposed and control conditions and a single constituent concentration as the predictor. Then, a novel multivariate analysis of exposure composition and health was used based on random forests, a recent extension of classification and regression trees that were applied to the outcome differences. For each exposure constituent, this approach yielded a nonparametric measure of the importance of that constituent in predicting differences in response on a given day, controlling for the other measured constituent concentrations in the model. Finally, an R2 analysis compared the relative importance of exposure scenario, plant, and constituent concentrations on each outcome. Peak expiratory flow is used to demonstrate how the multiple levels of the analysis complement each other to assess constituents most strongly associated with health effects.
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影响因子：
2.1
作者：
Godleski, John J.;Rohr, Annette C.;Koutrakis, Petros
通讯作者：
Koutrakis, Petros
影响因子：
2.1
作者：
Diaz EA;Lemos M;Coull B;Long MS;Rohr AC;Ruiz P;Gupta T;Kang CM;Godleski JJ
通讯作者：
Godleski JJ
DOI：
10.1093/biostatistics/2.3.337
发表时间：
2001-09-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Coull, B A;Schwartz, J;Wand, M P
通讯作者：
Wand, M P
影响因子：
2.1
作者：
Nikolov, Margaret C.;Coull, Brent A.;Godleski, John J.
通讯作者：
Godleski, John J.
影响因子：
2.9
作者：
Ye Y;Zhong X;Zhang H
通讯作者：
Zhang H