Statistical methods for large n and p problems

大型 n 和 p 问题的统计方法

基本信息

批准号：
9134138
负责人：
BRIAN Scott CAFFO
金额：
$ 6.04万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2017-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9134138
关键词：
Accelerometer Aging Alzheimer&apos s disease risk Biological Biotechnology Cardiac health Clinical Clinical Research Cohort Studies Collection Communities Data Data Analyses Data Set Development Diffusion Magnetic Resonance Imaging Dimensions Eastern Cooperative Oncology Group Electroencephalography Electrophysiology (science)Foundations Frequencies Functional Magnetic Resonance Imaging Goals Grant Home environment Image Individual Lead Location Longitudinal Studies Magnetic Resonance Imaging Measurement Measures Medical Methods Modeling Observational Study Phase Polysomnography Population Principal Component Analysis Research Research Personnel Running Sampling Scanning Series Signal Transduction Sleep Source Statistical Methods Structure Time Validation Variant Voice Work abstracting base blind computer framework density independent component analysis morphometry multilevel analysis neuroimaging new technology next generation novel public health research research study simulation success terabyte theories

项目摘要

DESCRIPTION (provided by applicant): Abstract Modern observational and experimental biological data has undergone a revolution. Driven by new biotechnology and computing advances, high dimensional, high density, functional multilevel and longitudinal biological signals are becoming commonplace in medical and public health research. These types of signals historically occurred in small clinical or experimental settings, often referred to as the "small n, large p" problem. We view the extension of these biological signals to cohort studies with longitudinal or hierarchical structure as a next generation of biostatistical problems. We've taken to calling this the "hierarchical large n, large p" problem. The goal of this grant is to introduce general methods for analyzing this form of biostatistical data. We propose three major aims for the analysis of multilevel or longitudinally collected biosignals. The first extends multilevel functional principal components, the investigators' generalization of functional principal components, to longitudinal and high dimensional settings. The second considers the investigators bi-directional filtering and extends it in high-dimensional and longitudinal settings. The third considers model-based independent component blind source separation and extends it to longitudinal settings. To solve this aim, we will also consider the fundamental problem of running MCMC samplers for high dimensional parameter spaces. Specifically, no current work exists for convergence control when the number of parameters is larger than the number of iterations. We propose a method of convergence control using finite population sampling. Our methods will be applied to unique data sets involving imaging (MRI, fMRI, DTI), electrophysiology (EEG, ECOG), sleep measurement (polysomnography) and novel measurements of aging (accelerometer). In the preliminary results, we demonstrate our capacity for working with such data with novel findings in the analysis of EEG, MRI and fMRI data sets. Methods such as unsupervised clustering, blind source separation and dimension reduction are generally recognized first steps in analyzing high dimensional data, and have been applied success- fully in an amazingly diverse collection of settings. Our proposal generalizes these basic approaches to high dimensional data while considering hierarchical and longitudinal directions of variation. Hence, our approaches will form a basic foundation for next generation biomedical functional data.

描述（由申请人提供）：抽象的现代观察和实验生物学数据已经发生了革命。在新的生物技术和计算进步的驱动下，高维，高密度，功能多级和纵向生物学信号在医学和公共卫生研究中变得司空见惯。这些类型的信号历史上发生在小的临床或实验环境中，通常称为“小N，大P”问题。我们将这些生物信号的扩展为具有纵向或分层结构的队列研究是下一代生物统计问题。我们将其称为“分层大N，大P”问题。该赠款的目的是引入一般方法来分析这种形式的生物统计数据。我们提出了三个主要目标，以分析多级或纵向收集的生物信号。第一个将多级功能主成分（研究者对功能主组件的概括）扩展到纵向和高维设置。第二个考虑了研究人员双向滤波，并在高维和纵向环境中扩展了它。第三个考虑了基于模型的独立组件盲源分离，并将其扩展到纵向设置。为了解决这个目标，我们还将考虑为高维参数空间运行MCMC采样器的基本问题。具体而言，当参数数量大于迭代次数时，没有目前的收敛控制工作。我们提出了一种使用有限种群抽样的收敛控制方法。我们的方法将应用于涉及成像（MRI，fMRI，DTI），电生理学（EEG，ECOG），睡眠测量（多性测量）和衰老的新测量（加速度计）的独特数据集。在初步结果中，我们证明了在EEG，MRI和FMRI数据集分析中使用新发现的此类数据的能力。通常在分析高维数据的方面公认了诸如无监督聚类，盲源分离和降低维度的方法，并已成功地应用于多样化的设置集合中。我们的建议将这些基本方法推广到高维数据的同时，同时考虑了变异的层次结构和纵向方向。因此，我们的方法将构成下一代生物医学功能数据的基本基础。