Development and Testing of Response Surface Methods for Investigating the Epidemiology of Exposure to Mixtures

用于调查混合物暴露流行病学的响应面方法的开发和测试

• 批准号: 9439849
• 负责人: Thomas F Webster
• 金额: $ 44.62万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9439849
• 关键词: Address; Benchmarking; Chemical Exposure; Chemicals; Cohort Studies; Complex; Computing Methodologies; Data; Data Set; Development; Dimensions; Disease; Educational workshop; Environment; Environmental Epidemiology; Epidemiologist; Epidemiology; Exposure to; Generations; Goals; Graph; Health; Human; Influentials; Intermediate Variables; Link; Mathematics; Measures; Methods; Modeling; National Institute of Environmental Health Sciences; Outcome; Outcome Measure; Reproductive Health; Research; Research Personnel; Science; Shapes; Structure; Sum; Surface; Testing; Time; Toxicology; base; computer code; conditioning; environmental chemical; epidemiologic data; epidemiology study; flexibility; indexing; insight; meetings; method development; novel; response; statistics; tool

According to NIEHS, “It is imperative to develop methods to assess the health effects associated with complex exposures in order to minimize their impact on the development of disease.” NIEHS has held several meetings on mixtures, including the 2015 workshop on Statistical Approaches for Assessing Health Effects of Environmental Chemical Mixtures in Epidemiology Studies. Conclusions include the following. 1) An interdisciplinary perspective is needed, including insights from environmental epidemiology, statistics/mathematics, toxicology and exposure science. 2) Mixtures epidemiology has three key goals: a) identify components of a mixture contributing to the outcome; b) examine interactions between the components; c) construct summary measures of exposure where possible. 3) Different methods have different strengths and weaknesses that may be complementary. We propose to build upon three methods that performed well at the 2015 workshop: Bayesian kernel machine regression (BKMR), exposure space smoothing (ESS) and weighted quantile sum regression (WQS). We will develop two complementary methods: 1) BKMR/ESS. We will expand and combine aspects of BKMR and ESS into one method that primarily addresses the first two goals: variable selection and interactions. Crucial aspects of our proposal are i) extension to binary health outcomes, the most common type of outcome data in epidemiology (the 2015 NIEHS workshop examined continuous outcomes); ii) variable selection using the hierarchical structures observed for correlations between exposures; iii) incorporation of toxicological information. 2) Single index model: We will evaluate a generalization of WQS, the single index model (SIM). SIM non-parametrically estimates a one-dimensional smooth function of a weighted sum of exposures. The weighted sum represents a summary measure of exposure (one based on toxicological principles), a third goal of mixtures epidemiology. Following method development, we will test the methods using both synthetic and real world data sets, including the Environment And Reproductive Health (EARTH) cohort study. We will incorporate causal inference tools such as directed acyclic graphs (DAGs). For example, correlated exposures (co-exposures) are confounders under some DAGs and colliders or intermediate variables under others. This must be taken into account in both generation of synthetic data and proper interpretation of results. The specific aims of this project are as follows: Specific Aim 1: Combine features of BKMR and ESS to produce a method for analyzing epidemiologic data that incorporates toxicological information; can handle continuous, binary and repeated measures outcome data; select important exposure variables; flexibly model and examine interactions; adjust for confounders; is robust to influential points; Specific Aim 2: Evaluate the single index model (SIM) as a method for analyzing epidemiologic mixtures data and generating exposure summary measures; Specific Aim 3: Make benchmark synthetic data and method computer code publicly available. ! 1!

根据NIEHS的说法，“当务之急是制定方法来评估与复杂性有关的健康影响。 以便将其对疾病发展的影响降至最低。NIEHS已经举行了几次会议 关于混合物，包括2015年关于评估对健康的影响的统计方法的讲习班 环境化学混合物在流行病学研究中的应用。结论包括以下几点。1)一个 需要跨学科的视角，包括来自环境流行病学的见解， 统计学/数学、毒理学和接触科学。2)混合物流行病学有三个关键目标：a) 确定对结果有贡献的混合物的成分；b)检查 各组成部分；c)在可能的情况下构建风险暴露的综合衡量标准。3)方法不同，方法也不同。 优势和劣势可能是互补的。我们建议在以下三种方法的基础上构建 在2015年的研讨会上表现良好：贝叶斯核机器回归(BKMR)，曝光空间 平滑(ESS)和加权分位数和回归(WQS)。我们将开发两种相辅相成的方法： 1)BKMR/ESS。我们将扩展并将BKMR和ESS的各个方面合并为一个方法，该方法主要 解决了前两个目标：变量选择和交互。我们提案的关键方面是i) 扩展到二元健康结果，这是流行病学中最常见的结果数据类型(2015 NIEHS讲习班审查了连续成果)；二)利用等级结构选择变量 观察暴露之间的相关性；iii)纳入毒理学信息。2)单一指标 模型：我们将评估WQS的一个推广，即单指数模型(SIM)。非参数SIM卡 估计曝光加权和的一维光滑函数。加权和表示一个 混合物流行病学的第三个目标--暴露的概要测量(基于毒理学原理)。 在方法开发之后，我们将使用合成数据集和真实世界数据集来测试这些方法， 包括环境和生殖健康(地球)队列研究。我们将纳入因果关系 推理工具，如有向无环图(DAG)。例如，相关暴露(联合暴露)是 一些DAG下的混杂因素和其他DAG下的对撞器或中间变量。必须考虑到这一点 在合成数据的生成和对结果的适当解释方面都有考虑。这样做的具体目的是 具体目标1：结合BKMR和ESS的特点，提出了一种分析方法 包含毒理学信息的流行病学数据；可以处理连续、二进制和重复的数据 衡量结果数据；选择重要的暴露变量；灵活地建模和检查交互作用；调整 对于混杂因素；对有影响力的点是稳健的；具体目标2：将单指数模型(SIM)作为 分析流行病学混合数据和生成暴露总结措施的方法.特殊目的 3：公开基准合成数据和方法计算机代码。 ！1！