SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES

模拟随时间变化的环境暴露的半参数方法 摘要 在人类的整个生命过程中，无数次的环境暴露影响着人类的发展 以及对疾病的易感性。评估多个暴露如何相互协同或对抗以影响 人类健康是了解环境暴露的全面影响的有力途径。 这种方法将需要在暴露组研究的新范式中使用，它涵盖了所有非基因的 疾病的原因，从外部自然环境到社会环境，从内部宏观到 微环境。然而，这种特殊的数据结构给许多环境调查带来了挑战 以及与时间相关的暴露的特征。特别是，多重环境暴露：1)是相互关联的 相关；2)依赖于时间；3)呈现时变效应；4)具有异质效应；以及5) 展示复杂和非线性的暴露-反应关系。统计数据的极限可得性 处理这些挑战的模型和分析工具阻碍了我们进行推理或绘制的能力 关于多次接触对人类健康影响的结论。在我们开发经验的基础上 统计方法，并受到我们在以下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，以解决重要的 环境健康研究中的科学问题，特别是通过以下目的：1)评估 时变环境暴露对时不变健康结局的影响并确定关键因素 脆弱性窗口；2)表征依赖时间的环境暴露对时间间隔的影响 事件和纵向结果；3)调查环境暴露对环境的异质性影响 子群体；以及4)开发、分发和支持建议的开源软件包 方法：研究方法。所有建议的模型、评估和测试程序都将通过分析 方法、理论推论、数值模拟和对多个数据集的应用 以人为对象的研究。拟议项目完成后，将提供新的统计方法，既 支持正在进行的合作，以解决关键的科学问题并使环境健康 研究界更好地评估依赖时间的暴露的影响。

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.