Statistical methods for large n and p problems

大型 n 和 p 问题的统计方法

• 批准号: 8321037
• 负责人: BRIAN Scott CAFFO
• 金额: $ 34.2万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2016-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8321037
• 关键词: Aging; Alzheimer' s disease risk; Biological; Biotechnology; 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; Health; Heart; 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 relevance; public health research; research study; simulation; success; theories

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. PUBLIC HEALTH RELEVANCE: Modern observational data is often longitudinal or multilevel functional biological signals. We propose a foundational approach for the analysis of such data, including scalable computing to next generation data sets.

描述（由申请人提供）：摘要现代观察和实验生物学数据经历了一场革命。在新的生物技术和计算技术的推动下，高维、高密度、多层次和纵向的生物信号在医学和公共卫生研究中变得越来越普遍。这些类型的信号历史上发生在小的临床或实验环境中，通常被称为“小n，大p”问题。我们认为，这些生物信号的扩展到队列研究的纵向或分层结构作为下一代的生物统计问题。我们称之为“分层大n，大p”问题。这项资助的目的是介绍分析这种形式的生物统计数据的一般方法。 我们提出了三个主要目标的多层次或纵向收集的生物信号的分析。第一个扩展多层次的功能主成分，调查人员的功能主成分的概括，纵向和高维设置。第二个考虑调查人员双向过滤，并将其扩展到高维和纵向设置。第三个考虑基于模型的独立分量盲源分离，并将其扩展到纵向设置。为了解决这一目标，我们还将考虑运行MCMC采样器的高维参数空间的基本问题。具体地说，没有当前的工作存在的收敛控制时，参数的数量大于迭代次数。我们提出了一种使用有限总体抽样的收敛控制方法。 我们的方法将被应用到独特的数据集，包括成像（MRI，fMRI，DTI），电生理（EEG，ECOG），睡眠测量（多导睡眠图）和新的测量老化（加速度计）。在初步的结果中，我们证明了我们的能力与新的发现，在EEG，MRI和fMRI数据集的分析，这样的数据。 无监督聚类、盲源分离和降维等方法通常被认为是分析高维数据的第一步，并已成功地应用于各种各样的环境中。我们的建议将这些基本方法推广到高维数据，同时考虑层次和纵向的变化方向。因此，我们的方法将为下一代生物医学功能数据奠定基础。 公共卫生相关性：现代观察数据通常是纵向或多层次的功能性生物信号。我们提出了一个基本的方法来分析这些数据，包括可扩展的计算到下一代数据集。