Adaptive Methodology for Functional Biomedical Data

功能生物医学数据的自适应方法

• 批准号: 7008195
• 负责人: JEFFREY S MORRIS
• 金额: $ 21.97万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2008-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7008195
• 关键词: clinical research; computer data analysis; computer program /software; computer system design /evaluation; data collection methodology /evaluation; human data; mathematical model; neoplasm /cancer epidemiology; statistics /biometry

DESCRIPTION (provided by applicant): An ever-increasing number of biomedical studies yield functional data, in which the ideal units of observation are curves. The goal of this research program is to develop new Bayesian methodology that provides a unifying framework for performing nonparametric estimation and inference for samples of curves. These methods will be flexible enough to model functions obtained from a variety of experimental designs, provide answers to a broad range of research questions, and will be sufficiently adaptive to apply to functional data from a wide range of applications. We will apply these methods to model functional data from a series of cancer-related biomedical studies that have motivated our methodological thinking. The methods we propose are appropriate for functional data characterized by numerous local features like peaks since we employ adaptive regularization procedures which denoise the functions with minimal attenuation of the dominant local features. The specific aims of this research are: 1. Introduce a unified functional mixed model framework for modeling samples of curves. Develop a wavelet-based method to fit this model and obtain adaptively regularized nonparametric estimates and Bayesian inference for fixed and random effect functions as well as covariance parameters. 2. Develop methodology to perform formal Bayesian inference and model selection in functional mixed models. Applications of this method include testing functional hypotheses on fixed/random effects, comparing models with different covariance structures, determining the number of basis functions, testing for correlation among different functional responses, and identifying appropriate piecewise constant compartment models. 3. Develop methods to perform wavelet-regularized functional principal component analysis. Extend our wavelet-based functional mixed model methods developed in Specific Aims 1 and 2 to other basis functions, including wavelet-regularized eigen functions and splines. 4. Apply the methods we develop in Specific Aims 1, 2, and 3 to a series of biomedical applications involving functional data, including colon carcinogenesis studies, a Planet Health children's activity study, an animal study investigating acute renal failure, and medical studies involving proteomics. 5. Produce publicly available statistical software for implementing the methods developed in this proposal.

描述（由申请人提供）：生物医学研究的数量不断增加，其中理想的观察单位是曲线。该研究计划的目的是开发新的贝叶斯方法，该方法为执行非参数估计和对曲线样本的推断提供了一个统一的框架。这些方法将足够灵活，可以建模从各种实验设计获得的功能，为广泛的研究问题提供答案，并具有足够的适应性，可以适用于广泛应用程序的功能数据。我们将应用这些方法来对一系列与癌症相关的生物医学研究的功能数据进行建模，这些研究激发了我们的方法论思维。我们提出的方法适用于功能数据，其特征在于众多局部特征（例如峰值），因为我们采用自适应正则化程序，以最小的局部特征衰减来证明功能。这项研究的具体目的是： 1。引入一个统一的功能混合模型框架，用于建模曲线样品。开发一种基于小波的方法来拟合此模型并获得适应性的正规化非参数估计值，并针对固定和随机效应函数以及协方差参数进行贝叶斯推断。 2。开发方法，以在功能混合模型中执行正式的贝叶斯推理和模型选择。该方法的应用包括在固定/随机效应上测试功能假设，比较具有不同协方差结构的模型，确定基本功能的数量，测试不同功能响应之间的相关性以及确定适当的分段恒定隔室模型。 3。开发执行小波调查功能主成分分析的方法。将在特定目标1和2中开发的基于小波的功能混合模型方法扩展到其他基础函数，包括小波调查的特征函数和花朵。 4.将我们在特定目标1、2和3中应用的方法应用于涉及功能数据的一系列生物医学应用，包括结肠致癌研究，一项行星健康儿童活动研究，研究急性肾衰竭的动物研究以及涉及蛋白质组学的医学研究。 5。生产公开可用的统计软件，用于实施本提案中开发的方法。