Extending the Distributed Lag Model framework to handle chemical mixtures
Extending the Distributed Lag Model framework to handle chemical mixtures
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DOI：
10.1016/j.envres.2017.03.031
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发表时间：
2017-07-01
影响因子：
8.3
通讯作者：
Gennings, Chris
中科院分区：
文献类型：
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作者：
Bello, Ghalib A.;Arora, Manish;Gennings, Chris
分布滞后模型（DLM）用于环境健康研究，以分析暴露对感兴趣结果的时间延迟效应。鉴于越来越需要的分析工具，暴露于多污染物混合物的影响进行评估，本研究试图扩展经典的DLM框架，以适应和评估多个纵向观察到的曝光。我们介绍了2种技术，用于量化多次曝光对感兴趣结果的时变混合效应。滞后WQS是第一种技术，基于加权分位数和（WQS）回归，这是一种使用加权指数估计混合效应的惩罚回归方法。我们还介绍了基于树的DLMs，一个非参数的替代评估滞后的混合效应。这种技术是基于随机森林（RF）算法，一种非参数，基于树的估计技术，在各种各样的领域表现出优异的性能。在模拟研究中，我们测试了这些技术的可行性，并评估其性能相比，标准的方法。这两种方法表现出相对稳健的性能，准确地捕捉预定义的非线性函数关系，在不同的模拟设置。此外，我们将这些技术应用于围产期暴露于环境金属毒物的数据，目的是评估暴露对神经发育的影响。我们的方法确定了关键的神经发育窗口，显示出对金属混合物的显着敏感性。
Distributed Lag Models (DLMs) are used in environmental health studies to analyze the time-delayed effect of an exposure on an outcome of interest. Given the increasing need for analytical tools for evaluation of the effects of exposure to multi-pollutant mixtures, this study attempts to extend the classical DLM framework to accommodate and evaluate multiple longitudinally observed exposures. We introduce 2 techniques for quantifying the time-varying mixture effect of multiple exposures on an outcome of interest. Lagged WQS, the first technique, is based on Weighted Quantile Sum (WQS) regression, a penalized regression method that estimates mixture effects using a weighted index. We also introduce Tree-based DLMs, a nonparametric alternative for assessment of lagged mixture effects. This technique is based on the Random Forest (RF) algorithm, a nonparametric, tree-based estimation technique that has shown excellent performance in a wide variety of domains. In a simulation study, we tested the feasibility of these techniques and evaluated their performance in comparison to standard methodology. Both methods exhibited relatively robust performance, accurately capturing pre-defined non-linear functional relationships in different simulation settings. Further, we applied these techniques to data on perinatal exposure to environmental metal toxicants, with the goal of evaluating the effects of exposure on neurodevelopment. Our methods identified critical neurodevelopmental windows showing significant sensitivity to metal mixtures.