COVARIANCE ESTIMATION FOR LONGITUDINAL CANCER DATA

纵向癌症数据的协方差估计

• 批准号: 6288245
• 负责人: Michael J Daniels
• 金额: $ 8.95万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-01 至 2004-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6288245
• 关键词: AIDS therapy; HIV infections; alpha benzopyrone; antidepressants; biomarker; combination therapy; endocrine disorder; gastrointestinal neoplasms; human data; longitudinal human study; major depression; mathematical model; mental disorder chemotherapy; metastasis; method development; model design /development; neoplasm /cancer chemotherapy; neoplasm /cancer epidemiology; psychotherapy; quality of life; statistics /biometry; zidovudine

DESCRIPTION (Applicant's abstract): In statistical modeling the covariance structure is often considered a nuisance, or at least of secondary importance to the mean. However, when the goals of an analysis include estimation of subject-specific effects or prediction, estimation of the covariance structure is very important. This project will develop methodology for improving estimation of covariance structure in longitudinal cancer studies. This will result in increased efficiency in estimation of fixed and random effects, such as subject-specific trajectories, and predictions. Specifically, this project will develop estimators of covariance matrices that are robust to a variety of eigenstructures and/or structural assumptions, develop methods to compute these estimators in hierarchical models, and develop classes of models to account for heterogeneity, both explained and unexplained by covariates, in covariance structures across subjects in longitudinal trials. In sum, this will provide greater flexibility in modeling, and efficiency in making inferences from, longitudinal cancer data. The methods include development of sensible prior distribution from which estimators can be derived that have good properties in small samples and/or in high dimensions and construction of hierarchical models to account for heterogeneous covariance structures across subjects through covariates and/or prior distributions. The common theme will be prior distributions which sh6nk the cova6ance matrix or function toward some parametric form or to some 'average' matrix or function, with the amount of shrinkage determined by the data. Computationally efficient ways to compute the estimators and fit the models will be explored, some of which will involve only minor modifications to standard software.

描述（申请人的摘要）：在统计建模中，协方差 结构通常被认为是一种麻烦，或者至少是次要的 到平均值。然而，当分析的目标包括估计 特定主题的影响或预测，协方差结构的估计 非常重要。该项目将开发改进方法 纵向癌症研究中协方差结构的估计。这将 导致固定和随机效应估计的效率提高，例如 作为特定主题的轨迹和预测。具体来说，这个项目 将开发协方差矩阵的估计器，该估计器对各种 特征结构和/或结构假设，开发计算这些的方法 分层模型中的估计器，并开发模型类别来解释 协方差中的异质性，由协变量解释和未解释 纵向试验中跨受试者的结构。总之，这将提供 建模更加灵活，推理效率更高， 纵向癌症数据。 这些方法包括开发合理的先验分布，其中 可以推导出在小样本和/或中具有良好特性的估计量 高维度和层次模型的构建来解释 通过协变量和/或跨受试者的异质协方差结构 先验分布。共同主题是 sh6nk 的先前发行版 协方差矩阵或函数朝向某种参数形式或某种 “平均”矩阵或函数，收缩量由 数据。计算估计量并拟合的有效计算方法 将探索模型，其中一些仅涉及对模型的微小修改 标准软件。