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: 10088444
• 负责人: Thomas F Webster
• 金额: $ 40.43万
• 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 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10088444
• 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，“必须制定方法来评估与复杂的 以尽量减少其对疾病发展的影响。”NIEHS已经举行了几次会议 包括2015年关于评估混合物对健康影响的统计方法的研讨会 流行病学研究中的环境化学混合物。结论如下。1)一个 需要跨学科的视角，包括环境流行病学的见解， 统计学/数学、毒理学和接触科学。2)混合流行病学有三个关键目标： 确定对结果有贡献的混合物的成分; B）检查 （c）尽可能建立暴露的汇总衡量标准。3)不同的方法有不同的 可以互补的优势和劣势。我们建议采用三种方法， 在2015年研讨会上表现良好：贝叶斯核机器回归（BKMR），暴露空间 平滑（ESS）和加权分位数和回归（WQS）。我们将开发两种互补的方法： 1)我们将扩展和联合收割机方面的BKMR和ESS到一个方法，主要 解决了前两个目标：变量选择和交互。我们的建议的关键方面是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年 NIEHS研讨会审查了连续的结果）; ii）使用分层结构的变量选择 观察暴露之间的相关性; iii）纳入毒理学信息。2)单个索引 模型：我们将评估WQS的推广，即单指数模型（SIM）。非参数SIM 估计暴露的加权和的一维平滑函数。加权和表示 混合物流行病学的第三个目标是对接触情况进行概括性衡量（根据毒理学原则）。 在方法开发之后，我们将使用合成和真实的世界数据集来测试这些方法， 包括环境与生殖健康（地球）队列研究。我们将把因果关系 推理工具，如有向无环图（DAG）。例如，相关暴露（共同暴露）是 一些DAG下的混杂变量和其他DAG下的碰撞器或中间变量。这一点必须考虑到 在生成合成数据和正确解释结果方面都要考虑到这一点。具体目标是 具体目标1：联合收割机BKMR和ESS的特点，提出一种基于BKMR的ESS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 测量结果数据;选择重要的暴露变量;灵活建模和检查相互作用;调整 对于混杂因素;对有影响力的点具有稳健性;具体目标2：将单指数模型（SIM）作为 分析流行病学混合数据并生成暴露汇总指标的方法;具体目标 3：公开基准综合数据和方法计算机代码。 !一个！