基于结构贝叶斯回归树对的围产期混合环境敏感性临界窗口估计
Estimating Perinatal Critical Windows of Susceptibility to Environmental Mixtures via Structured Bayesian Regression Tree Pairs
摘要：
母亲在怀孕期间接触环境化学品可能会改变分娩和儿童的健康结果。研究旨在确定暴露可能改变未来健康结果的关键窗口和时间段，并估计风险-反应关系。现有的统计方法侧重于估计在高时间分辨率下观察到的母亲接触单一环境化学品（如整个怀孕期间每周一次）与儿童健康结果之间的关联。扩展到在高时间分辨率下观察到的多种化学品会带来维度问题，并且缺乏统计方法。我们提出了一个基于树的模型，在高时间分辨率下观察到的混合曝光。所提出的方法使用的结构化的树对，定义结构化的主效应和时间分辨的预测变量和变量选择之间的相互作用，选择出的模型预测不相关的结果的加法合奏。我们将我们的方法应用于模拟和分析在科罗拉多州丹佛市出生队列中，在整个妊娠期间每周测量的五次暴露与出生体重之间的关系。我们确定了细颗粒物，二氧化硫和温度与出生体重呈负相关的关键窗口以及细颗粒物和温度之间的相互作用。软件在R软件包dlmtree中提供。
英文摘要：
Maternal exposure to environmental chemicals during pregnancy can alter birth and children's health outcomes. Research seeks to identify critical windows, time periods when the exposures can change future health outcomes, and estimate the exposure-response relationship. Existing statistical approaches focus on estimation of the association between maternal exposure to a single environmental chemical observed at high-temporal resolution, such as weekly throughout pregnancy, and children's health outcomes. Extending to multiple chemicals observed at high temporal resolution poses a dimensionality problem and statistical methods are lacking. We propose a tree-based model for mixtures of exposures that are observed at high temporal resolution. The proposed approach uses an additive ensemble of structured tree-pairs that define structured main effects and interactions between time-resolved predictors and variable selection to select out of the model predictors not correlated with the outcome. We apply our method in a simulation and the analysis of the relationship between five exposures measured weekly throughout pregnancy and resulting birth weight in a Denver, Colorado birth cohort. We identified critical windows during which fine particulate matter, sulfur dioxide, and temperature are negatively associated with birth weight and an interaction between fine particulate matter and temperature. Software is made available in the R package dlmtree.
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