Statistical Methods for Multilevel Multivariate Functional Studies

描述（由申请人提供）：来自流行病学和医学研究的证据往往是重大政策决定的核心。现代研究数据变得越来越复杂，提出了非传统建模和推理的挑战。特别是，技术和计算的进步使得功能数据的记录和处理成为可能。功能数据的一些示例是脑电图（EEG）活动的时间序列、解剖形状和功能性MRI。在这种情况下，越来越有必要调整现有的工具和设计新的统计方法来提取信息，而无需对人口或受试者的功能特征进行严格的参数假设。本提案的目的是开发统计模型的类别，用于从具有大量受试者和受试者内和受试者间异质性的单级（一次访视时每个受试者的一个或多个功能）和多级（多次访视时每个受试者的一个或多个功能）功能数据中提取特征。具体来说，我们的动机是我们正在进行的关于睡眠和不良健康结果之间关系的研究。这些研究对公共卫生的主要好处是增加了我们对睡眠对健康结果影响的认识。开发更好地了解一般人群睡眠结构的方法将允许进一步确定医学疾病如何干扰睡眠以及睡眠中断是否与健康相关的后果有关。所提出的方法是非常普遍的，将影响许多其他领域的科学研究。将直接受益于拟议研究的科学数据的两个例子是大脑，心脏等的磁共振成像（MRI）和多次访问记录的每日血糖轨迹。

DESCRIPTION (provided by applicant): Evidence from epidemiological and medical research is often central to major policy decisions. Modern research data have become increasingly complex, raising non-traditional modeling and inferential challenges. In particular, advancements in technology and computation have made recording and processing of functional data possible. Some examples of functional data are time series of electroencephalographic (EEG) activity, anatomical shape, and functional MRI. In this context it has become increasingly necessary to adapt existent tools and design new statistical methods to extract information without stringent parametric assumptions about the population or subject functional characteristics. The purpose of this proposal is to develop classes of statistical models for feature extraction from single-level (one or multiple functions per subject at one visit) and multilevel (one or multiple functions per subject at multiple visits) functional data having a large number of subjects and large within- and between-subject heterogeneity. Specifically, we are motivated by our ongoing studies of the association of sleep and adverse health outcomes. PUBLIC HEALTH RELEVANCE the major benefits of these studies to public health will be to increase our knowledge concerning the effects of sleep on health outcomes. Development of methods to better understand the sleep architecture in the general population will allow further determination of how medical disorders may disturb sleep and whether sleep disruption is related to health-related consequences. The methodology proposed is very general and will impact many other areas of scientific research. Two examples of scientific data that would directly benefit from the proposed research are Magnetic Resonance Imaging (MRI) of the brain, heart, etc. and daily blood glucose trajectories recorded at multiple visits.