Analysis of High-Dimensional Environmental Health Data

高维环境健康数据分析

• 批准号: 7032301
• 负责人: Brent Andrew Coull
• 金额: $ 19.02万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7032301
• 关键词: air pollution; clinical research; environmental exposure; environmental health; environmental toxicology; epidemiology; human data; longitudinal human study; mathematical model; model design /development; pollutant interaction; pollution related respiratory disorder; statistics /biometry

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.

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是复杂的。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，这些研究调查了随着时间的推移可能导致多种相互关联的变化的环境影响。实证数据分析将在每个具体目标中发挥核心作用。