Analysis of High-Dimensional Environmental Health Data

描述(由申请人提供)：本提案的目标是开发环境健康数据的统计分析方法，当感兴趣的健康影响复杂时。具体目标的动机是毒理学研究和环境流行病学研究中出现的问题，这些研究调查环境影响，这些影响可能会随着时间的推移导致多种相互关联的变化。经验数据分析将在每个具体目标中发挥核心作用。 环境流行病学家现在例行公事地对一系列生物终点实施分层研究设计。将开发一类用于多级别二进制和有序症状数据的通用分层模型，并将这些模型的性能与已知对建模假设敏感的现有方法进行比较。 流行病学研究一再表明，空气颗粒物与人类人群中发病率和死亡率的增加之间存在关联，特别是在先前存在呼吸或心脏脆弱性的受试者中。目前的实验室研究主要集中在生理机制上。将开发灵活的模型，以评估随着时间的推移暴露对多个生理终点的影响。

DESCRIPTION (provided by applicant): The goal of this proposal is to develop statistical analysis methods for environmental health data when the health effects of interest are complex. The Specific Aims are motivated by problems arising in toxicological studies and environmental epidemiological studies that investigate environmental effects that potentially result in multiple, interrelated changes over time. Empirical data analysis will play a central role in each of the Specific Aims. Environmental epidemiologists now routinely implement hierarchical study designs for an array o of biologic endpoints. A general class of hierarchical models for multilevel binary and ordinal symptom data will be developed, and the performance of these models will be compared to existing approaches known to be sensitive to modeling assumptions. Epidemiological studies have repeatedly shown associations between air particulate and increased morbidity and mortality in human populations, particularly in subjects with pre-existing respiratory or cardiac vulnerability. Current laboratory research focuses on the physiological mechanisms. Flexible models will be developed for assessing effects of exposure over time on multiple physiological endpoints.