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，“必须开发方法来评估与复杂相关的健康影响 暴露以最大程度地减少其对疾病发展的影响。” Niehs举行了几次会议 关于混合物，包括2015年关于评估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。结论包括以下内容。 1） 需要跨学科的观点，包括来自环境流行病学的见解， 统计/数学，毒理学和暴露科学。 2）混合物流行病学有三个关键目标：a） 确定有助于结果的混合物的组成部分； b）检查 成分; c）在可能的情况下构建暴露的摘要度量。 3）不同的方法具有不同的 可能完整的优势和劣势。我们建议以三种方法来建立 在2015年的研讨会上表现良好：贝叶斯内核机器回归（BKMR），曝光空间 平滑（ESS）和加权分位数总和回归（WQS）。我们将开发两种完整的方法： 1）BKMR/ESS。我们将扩展并将BKMR和ESS的各个方面扩展为主要的方法 解决前两个目标：变量选择和交互。我们提案的关键方面是我） 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研讨会检查了持续的结果）； ii）使用分层结构的变量选择 观察到暴露之间的相关性； iii）融合了毒理学信息。 2）单个索引 模型：我们将评估WQS的概括，即单个索引模型（SIM）。非参数 估计加权总和的一维平滑函数。加权和代表 摘要测量暴露（一种基于毒理学原理），这是混合流行病学的第三个目标。 在方法开发之后，我们将使用合成和现实世界数据集测试方法， 包括环境和生殖健康（Earth）队列研究。我们将加入催化 推理工具，例如有向无环图（DAG）。例如，相关的暴露（共曝光）为 在某些DAG和COLLIDERS或其他中间变量下的混淆者。必须将其纳入 在综合数据的产生和结果的正确解释中说明。这个特定的目的 项目如下：特定目标1：结合BKMR和ESS的特征以产生分析方法 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 衡量结果数据；选择重要的曝光变量；灵活建模并检查相互作用；调整 对于混杂的人；能够影响要点很强大；特定目标2：将单个索引模型（SIM）评估为 分析的流行病学混合物数据和产生暴露汇总测量的方法；具体目标 3：公开提供基准合成数据和方法计算机代码。 呢1！