Structured nonparametric methods for mixtures of exposures

混合暴露的结构化非参数方法

• 批准号: 10112908
• 负责人: David Brian Dunson
• 金额: $ 42.61万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10112908
• 关键词: Age; Air; Biological; Biometry; Birth Weight; Body mass index; Bypass; Chemical Exposure; Chemical Structure; Chemicals; Child; Code; Complex; Complex Mixtures; Data; Data Analyses; Data Analytics; Data Set; Data Sources; Dependence; Development; Dimensions; Disease; Dose; Eating; Environment; Environmental Epidemiology; Epidemiology; Exposure to; Food; Goals; Health; Individual; Knowledge; Lead; Literature; Measurement; Methods; Modeling; Modernization; National Health and Nutrition Examination Survey; Neurologic; Outcome; Output; Performance; Reproducibility; Research; Research Design; Risk; Science; Scientist; Shapes; Site; Source; Statistical Methods; Statistical Models; Structure; Surface; Testing; Theoretical Studies; Time; Toxicology; Training; Uncertainty; Visual; Work; analytical tool; base; cohort; design; epidemiologic data; epidemiology study; experimental study; exposed human population; holistic approach; improved; innovation; insight; interest; lectures; method development; novel; programs; response; routine practice; simulation; skills; software development; study population; synergism; user friendly software; user-friendly

PROJECT SUMMARY Although it is well known that humans are exposed to a complex mixture of different chemicals, having constit- uents that change dynamically as an individual ages, very little is known about how these exposures interact to impact health outcomes. The overarching focus in the toxicology and epidemiology literatures has been on ex- amining the health effects of chemicals one at a time. One reason for the lack of consideration of more holistic approaches for simultaneously assessing the health effect of multiple chemicals is the lack of appropriate sta- tistical methods that are interpretable and reliable at disentangling the impact of each chemical in the mixture. When attempts are made to include different chemicals simultaneously in statistical models, most of the focus has been on generic multivariate statistical methods that often fail to have adequate performance. For exam- ple, simply including different exposures in nonparametric regression models can lead to unstable estimates due to the so-called curse of dimensionality, particularly if the different exposures are moderately to highly cor- related. The overarching goal of this proposal is to develop novel statistical approaches, which are specifically tailored for mixture exposure problems, incorporating mechanistic constraints and supplemental data on chem- ical structure and toxicological responses to improve performance. An initial focus is on developing restricted nonparametric regression methods, which constrain the response surface to be monotone with possible down- turns at low and high doses, consistent with prior data and mechanistic knowledge. Such constraints substan- tially improve stability and performance in estimating dose response, while facilitating interpretation. Another key advance is the development of mechanistic interaction models, which reduce dimensionality and enable disentangling of main effects and chemical-chemical interactions, allowing no interaction, synergy or antago- nism. A further thread designs a novel class of mechanistic response surface models, which directly incorpo- rate supplemental data on chemical structure and borrow information from one-chemical-at-a-time toxicological studies. These models enable de novo prediction of dose response and interactions for new chemicals, which have known structure but have not been studied in toxicology and epidemiology studies. These predictions in- clude an accurate characterization of uncertainty, highlighting cases in which more data are needed. To be ap- propriate for a rich variety of epidemiological study designs, the methods are generalized to account for covari- ate adjustments, longitudinal and nested data structures, censoring, and other complications. A key focus of the project is on producing user-friendly software that non-statistician scientists can use to analyze and visual- ize the health effects of mixture exposures, provided on the project's GitHub site and beta tested. Methods will be tested in a multi-tiered fashion through theoretical studies, comprehensive simulation experiments including comparisons to a rich variety of existing approaches under challenging scenarios, and applications to multiple epidemiology studies. These studies include the MSSM Children's Cohort, NHANES, and CHAMACOS.

项目摘要 尽管众所周知，人类暴露于不同化学物质的复杂混合物，具有构成 随着个体年龄而动态变化的Uents，对于这些暴露方式如何相互作用，知之甚少 影响健康结果。毒理学和流行病学文献的总体重点一直在于 一次弥补化学物质的健康影响。缺乏更全面的考虑的原因之一 同时评估多种化学物质的健康效应的方法是缺乏适当的Sta- 可以解释且可靠地解开混合物中每种化学物质的影响的潮流方法。 尝试在统计模型中同时包括不同的化学物质时，大多数焦点 一直以来一直无法具有适当性能的通用多元统计方法。进行考试 PLE，仅在非参数回归模型中仅包括不同的暴露会导致不稳定的估计 由于所谓的维度诅咒，尤其是如果不同的暴露在高度高度的情况下 有关的。该提案的总体目标是开发新颖的统计方法，这是特别是 针对混合暴露问题量身定制，结合了机械限制和有关化学的补充数据 Ial结构和毒理学反应，以提高性能。最初的重点是发展受限制 非参数回归方法，它限制了响应表面是单调的 低剂量和高剂量的转弯，与先前的数据和机械知识一致。这样的约束代替 在促进解释的同时，在估计剂量反应方面的稳定性和性能。其他 关键进步是机械互动模型的发展，从而降低了维度并启用 分解主要作用和化学化学相互作用，不允许相互作用，协同作用或抗牙 nism。另一个线程设计了一类新型的机械响应表面模型，该模型直接不可能 从一次化学结构的速率补充数据和借用信息从一次化学毒理学中借用 研究。这些模型可以从头开始预测剂量反应和新化学品的相互作用， 已知结构，但尚未在毒理学和流行病学研究中进行研究。这些预测 闭合不确定性的准确表征，突出了需要更多数据的情况。是 适用于各种流行病学研究设计，这些方法被推广，以解释协价 ATE调整，纵向和嵌套数据结构，检查和其他并发症。一个重点 该项目正在生产非统计学家科学家可以用来分析和视觉的用户友好软件 Ize在项目的GitHub站点和测试beta测试中提供的混合物暴露的健康影响。方法将 通过理论研究以多层方式进行测试，全面的模拟实验包括 在具有挑战性的情况下，将各种现有方法与多种现有方法进行比较，并应用于多个 流行病学研究。这些研究包括MSSM儿童队列，NHANES和CHAMACOS。