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）：越来越多的生物医学研究产生功能数据，其中理想的观察单位是曲线。该研究计划的目标是开发新的贝叶斯方法，为曲线样本的非参数估计和推理提供统一的框架。这些方法将足够灵活，可以对从各种实验设计中获得的函数进行建模，为广泛的研究问题提供答案，并且具有足够的适应性，可以应用于来自广泛应用的函数数据。我们将应用这些方法对一系列癌症相关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考。我们提出的方法适用于以大量局部特征（如峰值）为特征的函数数据，因为我们采用自适应正则化程序，该程序以对主要局部特征的最小衰减来对函数进行去噪。本研究的具体目标是： 1. 引入统一的功能混合模型框架，用于对曲线样本进行建模。开发一种基于小波的方法来拟合该模型，并获得固定和随机效应函数以及协方差参数的自适应正则化非参数估计和贝叶斯推理。 2. 开发在功能混合模型中执行正式贝叶斯推理和模型选择的方法。该方法的应用包括测试固定/随机效应的功能假设、比较具有不同协方差结构的模型、确定基函数的数量、测试不同功能响应之间的相关性以及识别适当的分段恒定室模型。 3. 开发执行小波正则化函数主成分分析的方法。将我们在特定目标 1 和 2 中开发的基于小波的函数混合模型方法扩展到其他基函数，包括小波正则化本征函数和样条函数。 4. 将我们在具体目标 1、2 和 3 中开发的方法应用于涉及功能数据的一系列生物医学应用，包括结肠癌发生研究、地球健康儿童活动研究、调查急性肾衰竭的动物研究以及涉及蛋白质组学。 5. 制作可公开使用的统计软件，以实施本提案中开发的方法。