SEMIPARAMETRIC METHODS FOR MODELING OF TIME-DEPENDENT ENVIRONMENTAL EXPOSURES

时变环境暴露模拟的半参数方法 摘要 无数的环境暴露贯穿于人类的生命历程，影响着人类的发展 和对疾病的易感性。评估多重接触如何协同或相互对抗， 人类健康是了解环境暴露的全面影响的一个强有力的方法。 这种方法将需要在新的范式麻烦的研究，其中包括所有非遗传 从外部自然环境到社会环境，从内部宏观到微观， 微环境然而，许多环境调查都受到特殊数据结构的挑战 和时间依赖性暴露的特征。特别是，多重环境暴露：1）相互之间， 相关; 2）具有时间依赖性; 3）表现出随时间变化的效应; 4）具有异质性效应;以及5） 表现出复杂和非线性的响应关系。统计数据的有限性 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图的能力 关于多重暴露对人类健康影响的结论。根据我们的经验， 统计方法，并受到我们在许多合作中遇到的挑战的激励， 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 环境卫生研究中的科学问题，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， 脆弱性窗口; 2）描述时间依赖性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 4）开发，分发和支持开源软件包， 方法.所有提出的模型，估计和测试程序将通过分析研究， 方法，理论推理，数值模拟，并应用于多个数据集从批准 人体研究拟议项目完成后，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，并使环境健康 研究界应更好地评估与时间有关的照射的影响。

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.