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 虽然众所周知，人类暴露于不同化学物质的复杂混合物中，但其结构- 随着个体年龄的增长而动态变化的因素，很少有人知道这些暴露如何相互作用， 影响健康结果。毒理学和流行病学文献的总体重点是前- 消除化学物质对健康的影响一个原因是缺乏考虑更全面的 同时评估多种化学品对健康影响的方法缺乏适当的标准， 在解开混合物中每种化学品的影响方面，可以解释和可靠的统计方法。 当试图在统计模型中同时包括不同的化学品时，大多数关注点都集中在 一直是通用的多元统计方法，往往无法有足够的性能。为了考试- 简单地说，在非参数回归模型中简单地包括不同的暴露量可能会导致不稳定的估计 由于所谓的维数灾难，特别是如果不同的暴露是中度到高度相关的， 相关.该提案的总体目标是开发新的统计方法，具体如下： 为混合物暴露问题量身定制，结合机械约束和化学补充数据， 化学结构和毒理学反应，以提高性能。第一个重点是发展限制性的 非参数回归方法，它限制响应面是单调的， 在低剂量和高剂量下的变化，与先前的数据和机械知识一致。这些限制包括： 在估计剂量反应时，基本上提高了稳定性和性能，同时便于解释。另一 关键的进展是机械交互作用模型的发展，它减少了维度， 解开主要影响和化学-化学相互作用，不允许相互作用、协同作用或反作用， nism。进一步的线程设计了一类新的机制响应面模型，直接纳入， 对化学结构的补充数据进行评级，并从一次一种化学品的毒理学中借用信息 问题研究这些模型能够从头预测新化学品的剂量反应和相互作用， 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- 包括对不确定性的准确描述，强调需要更多数据的情况。为了- 适合于各种流行病学研究设计，该方法被推广到考虑协方差， ate调整、纵向和嵌套数据结构、删失和其他并发症。的一个关键重点 该项目是制作用户友好的软件，非统计学家的科学家可以用来分析和可视化， 评估混合物暴露对健康的影响，在该项目的GitHub网站上提供，并进行beta测试。方法将 通过理论研究、全面的模拟实验， 在具有挑战性的情况下，与各种现有方法进行比较，并应用于多种 流行病学研究。这些研究包括MSSM儿童队列、NHANES和CHAMACOS。