New functional models for biomedical data

生物医学数据的新功能模型

• 批准号: 7147732
• 负责人: WENSHENG GUO
• 金额: $ 11.97万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7147732
• 关键词: animal data; biomarker; brain neoplasms; computer assisted medical decision making; computer program /software; data collection methodology /evaluation; diabetes mellitus; diagnosis design /evaluation; ectopic pregnancy; human data; liver transplantation; mathematical model; model design /development; respiratory distress syndrome of newborn

DESCRIPTION (provided by applicant): Nonparametric functional models have a wide range of applications in cancer and other biomedical research, such as biomarker data, hormone profiles, circadian patterns and EEG data. Most of the current literature focuses on developing flexible nonparametric models for the mean structure while the stochastic variation around the mean is treated as a nuisance component. In many biomedical applications, such as the data described in this proposal, the inferential focus is on the stochastic variation and on how it is related to the covariates and experimental treatment. Our first specific aim is to develop methods to nonparametrically estimate the unknown covariance structure in the functional data analysis setting. The estimate of the covariance structure can in turn be used to obtain more efficient estimates of the mean parameters. Our second aim is to extend the concept of functional data analysis to time series data, in which the basic unit of the data analysis is a time series and the focus on the analysis is not on the mean, but on how the covariates are related to the stochastic variation over time. A time series is uniquely defined by its spectrum and when we average across a group of spectra, we can obtain a group-average spectrum that uniquely defines a group-average" time series. Functional linear models and functional mixed effects models can also be applied to the spectra to make inference on covariates and treatment effects. We will focus our methods development on nonstationary time series data, which are most common in biomedical research. We will also develop computationally efficient estimation procedures through construction of equivalent state space models. Our third specific aim is to generalize the concept of functional data analysis to density data, in which the basic unit of the data analysis is a density. The proposed density models allow us to investigate covariates or treatment effects without making explicit assumptions on the underlying distributions. The proposed methods are motivated by and will be applied to the clinical studies that the principal investigator is directly or indirectly involved.

描述（由申请人提供）：非参数函数模型在癌症和其他生物医学研究中具有广泛的应用，例如生物标志物数据、激素概况、昼夜节律模式和脑电图数据。当前大多数文献都集中于为均值结构开发灵活的非参数模型，而均值周围的随机变化被视为有害成分。在许多生物医学应用中，例如本提案中描述的数据，推理重点是随机变化以及它与协变量和实验治疗的关系。我们的第一个具体目标是开发在函数数据分析设置中非参数估计未知协方差结构的方法。协方差结构的估计又可以用于获得平均参数的更有效的估计。我们的第二个目标是将函数数据分析的概念扩展到时间序列数据，其中数据分析的基本单位是时间序列，分析的重点不是均值，而是协变量如何与随时间的随机变化。时间序列由其频谱唯一定义，当我们对一组频谱进行平均时，我们可以获得唯一定义组平均时间序列的组平均频谱。还可以应用函数线性模型和函数混合效应模型我们将把我们的方法开发重点放在生物医学研究中最常见的非平稳时间序列数据上，我们还将通过构建等效状态空间模型来开发计算高效的估计程序。具体目标是将函数数据分析的概念推广到密度数据，其中数据分析的基本单位是密度。所提出的密度模型使我们能够研究协变量或治疗效果，而无需对基础分布做出明确的假设。并将适用于主要研究者直接或间接参与的临床研究。