Statistical methods for large n and p problems

大型 n 和 p 问题的统计方法

• 批准号: 8146107
• 负责人: BRIAN Scott CAFFO
• 金额: $ 34.75万
• 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/8146107
• 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数据已经经历了一场革命。在新的生物技术和计算进步的推动下，高维、高密度、功能性多层次和纵向生物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型临床或实验环境中，通常称为“小 n，大 p”问题。我们将这些生物信号扩展到具有纵向或分层结构的队列研究作为下一代生物统计问题。我们将其称为“分层大 n，大 p”问题。这笔赠款的目标是引入分析这种形式的生物统计数据的通用方法。 我们提出了分析多级或纵向收集的生物信号的三个主要目标。第一个将多级功能主成分（研究人员对功能主成分的概括）扩展到纵向和高维设置。第二个考虑研究人员的双向过滤并将其扩展到高维和纵向设置。第三种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。为了实现这一目标，我们还将考虑针对高维参数空间运行 MCMC 采样器的基本问题。具体来说，当参数数量大于迭代次数时，当前不存在收敛控制的工作。我们提出了一种使用有限总体抽样的收敛控制方法。 我们的方法将应用于涉及成像（MRI、fMRI、DTI）、电生理学（EEG、ECOG）、睡眠测量（多导睡眠图）和新颖的衰老测量（加速计）的独特数据集。在初步结果中，我们通过分析脑电图、磁共振成像和功能磁共振成像数据集的新发现证明了我们处理此类数据的能力。 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，并且已成功应用于令人惊讶的多样化设置集合中。我们的建议将这些基本方法推广到高维数据，同时考虑变化的层次和纵向方向。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：现代观测数据通常是纵向或多层次的功能生物信号。我们提出了一种分析此类数据的基本方法，包括对下一代数据集的可扩展计算。