SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES

时变环境暴露建模的半参数方法

基本信息

批准号：
10552047
负责人：
Mengling Liu
金额：
$ 38.14万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-09 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10552047
关键词：
Address Adolescence Affect Arsenic Bangladesh Blood Pressure Characteristics Chemical Exposure Child Child Health Chronic Kidney Failure Collaborations Communities Complex Computer software Development Disease Environment Environmental Exposure Environmental Health Environmental Impact Event Exhibits Exposure to Family Goals Health Human Human Development Impairment Individual Intervention Investigation Life Cycle Stages Linear Regressions Longitudinal Studies Metabolic syndrome Methodology Methods Modeling Names National Institute of Environmental Health Sciences Outcome Pattern Predisposition Procedures Process Proportional Hazards Models Reduce health disparities Research Slice Social Environment Statistical Methods Statistical Models Structure Study Subject Subgroup Testing Time Vacuum Work analytical tool cohort data structure experience fire fighter flexibility hazard human subject improved indexing innovation interest multiple datasets non-genetic novel open source response semiparametric simulation synergism tool

项目摘要

SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES Abstract Countless environmental exposures throughout the human life course and influence human development and susceptibility to disease. Assessing how multiple exposures synergize or antagonize each other to affect human health is a powerful approach to understanding the all-around impact of environmental exposures. Such approach will be needed in the new paradigm of exposome research, which encompass all non-genetic causes for diseases, from external natural to social environments and from internal macro- to microenvironments. However, many environmental investigations are challenged by the special data structure and characteristics of time-dependent exposures. In particular, multiple environmental exposures: 1) are inter- correlated; 2) are time-dependent; 3) exhibit time-varying effects; 4) have heterogeneous effects; and 5) demonstrate complex and nonlinear exposure-response relationships. The limiting availability of statistical models and analytical tools to handle these challenges hinder our ability to make inference or draw conclusions about the effects of multiple exposures on human health. Building upon our experience developing statistical methodologies and motivated by the challenges encountered in many of our collaborations in environmental research, we propose to develop and implement novel statistical methods to address important scientific questions in environmental health research, specifically through the following aims: 1) evaluate the effects of time-dependent environmental exposures on time-invariant health outcomes and identify critical windows of vulnerability; 2) characterize the effects of time-dependent environmental exposures on time-to- event and longitudinal outcomes; 3) investigate the heterogeneous impacts by environmental exposures on subpopulations; and 4) develop, distribute, and support open-source software packages for the proposed methods. All proposed models, estimation, and testing procedures will be investigated through analytical approaches, theoretical inference, numerical simulations, and applications to multiple datasets from approved human-subject studies. Upon its completion, the proposed project will provide new statistical methods that both support ongoing collaborations to address critical scientific questions and enable the environmental health research community to better assess the impact of time-dependent exposures.

时变环境暴露建模的半参数方法抽象的人类生命历程中无数的环境暴露并影响着人类的发展和对疾病的易感性。评估多重暴露如何协同或拮抗彼此产生影响人类健康是了解环境暴露的全面影响的有效方法。暴露组研究的新范式将需要这种方法，该范式涵盖所有非遗传性疾病发生的原因，从外部自然环境到社会环境，从内部宏观环境到社会环境。微环境。然而，许多环境调查都受到特殊数据结构的挑战以及时间依赖性暴露的特征。特别是，多重环境暴露：1) 相关； 2) 具有时间依赖性； 3）表现出随时间变化的效果； 4）具有异质效应；和 5) 展示复杂且非线性的暴露-反应关系。统计数据的可用性有限应对这些挑战的模型和分析工具阻碍了我们进行推理或绘图的能力关于多重暴露对人类健康影响的结论。以我们的开发经验为基础统计方法，并受到我们在许多合作中遇到的挑战的推动环境研究，我们建议开发和实施新的统计方法来解决重要的问题环境健康研究中的科学问题，特别是通过以下目标：1）评估随时间变化的环境暴露对随时间变化的健康结果的影响，并确定关键的脆弱性之窗； 2) 描述随时间变化的环境暴露对时间的影响事件和纵向结果； 3）调查环境暴露对环境的异质影响亚人群； 4) 为拟议的项目开发、分发和支持开源软件包方法。所有提出的模型、估计和测试程序都将通过分析进行调查方法、理论推论、数值模拟以及对已批准的多个数据集的应用人体研究。完成后，拟议项目将提供新的统计方法，支持正在进行的合作，以解决关键的科学问题并促进环境健康研究界更好地评估时间依赖性暴露的影响。