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方法，当感兴趣的健康影响是复杂的。具体目标的动机是在空气传播的微粒物质对健康影响的毒理学和环境流行病学研究中出现的问题。该项目的具体目标是开发(i)基于小波的历史功能数据模型，以评估接触与健康之间的高维关联；用于分析动物颗粒物浓缩器研究中产生的多变量功能数据的层次隐马尔可夫模型；（三）解决微粒物质流行病学研究中因时空偏差而产生的暴露测量误差的方法。我们将提出的方法应用于几个现有分析方法不能充分利用数据的数据集，包括(i)来自敏感亚群物质的半连续心率变异性数据。在激励应用中，这些方法将为环境卫生研究中的两个科学紧迫问题提供见解：确定空气颗粒发病率和死亡率的生物学机制，以及确定造成观察到的健康影响的污染源。更一般地说，所提出的方法代表了功能数据分析、隐马尔可夫建模和测量误差建模领域的进步，这些领域适用于涉及高维数据的各种生物医学研究环境。

DESCRIPTION (provided by applicant): The goal of this proposal is to develop statistical methods for environmental health data when the health effects of interest are complex. The Specific Aims are motivated by problems arising in toxicological and environmental epidemiological studies of the health effects of airborne particulate matter. Specific aims of the project are the development of (i) Wavelet-based historical functional data models for assessing high-dimensional associations between exposure and health; (ii) Hierarchical hidden Markov models for analyzing multivariate functional data arising from animal particulate matter concentrator studies; (iii) Methods to address exposure measurement error arising from spatial and temporal misalignment in particulate matter epidemiology studies. We will apply the proposed methods to several data sets for which existing analysis methods do not make full use of the data, including (i) semi-continuous heart- rate variability data from matter in sensitive subpopulations. In the motivating applications, the methods will provide insight into two scientifically pressing issues in environmental health research: the identification of biologic mechanisms of morbidity and mortality of air particles, and identification of pollution sources responsible for observed health effects. More generally, the proposed methods represent advancements in the areas of functional data analysis, hidden Markov modeling, and measurement error modeling that are applicable in a variety of biomedical research settings involving high-dimensional data.