Semi-parametric joint models for longitudinal and time to event data

纵向和事件时间数据的半参数联合模型

• 批准号: 8897406
• 负责人: WENSHENG GUO
• 金额: $ 29.57万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8897406
• 关键词: Biological Markers; Cessation of life; Data; Data Analyses; Decision Making; Decision Modeling; Disease; End stage renal failure; Event; Failure; Goals; Health; Information Systems; Joints; Kidney; Kidney Transplantation; Linear Models; Link; Methods; Modeling; Motivation; Non-linear Models; Outcome; Patients; Pattern; Pennsylvania; Process; Public Domains; Recording of previous events; Series; Shapes; Survival Analysis; Theoretical Studies; Time; Transplant Recipients; Transplantation; United Network for Organ Sharing; United States; Universities; Vaginal delivery procedure; Wood material; Work; base; flexibility; liver transplantation; method development; non-Gaussian model; novel; simulation; software development; statistics; user friendly software

DESCRIPTION (provided by applicant): Semiparametric joint models for longitudinal biomarkers and time to event data The goal of this project is to develop novel statistics methods to jointly model longitudinal biomarker trajectories and time to event data. The proposed methods are motivated and will be applied to three major applications: 1) liver transplant and kidney transplant available through the United Network for Organ Sharing (UNOS), 2) the end stage renal disease (ESRD) data available through the United States Renal Data System (USRDS), and 3) the Vaginal birth after a prior cesarean (VBAC) data collected at the University of Pennsylvania. The main motivation comes from the fact that biomarkers are usually the surrogates of the underlying disease process and need to be treated as surrogate outcomes in modeling the time to event data, and the trajectories of the biomarkers usually require nonparametric models allowing flexible patterns over time, such as smooth curves, shape-registered curves, and branching curves. Another motivation is that in predicting the event such as death, the cumulative effects of the biomarkers may be more appropriate than the concurrent values, and therefore we propose to combine the ideas of functional data analysis and survival analysis. We will first develop the functional accelerated failure time (AFT) models and their join models with functional mixed effects models. We then extend this framework to include non-Gaussian longitudinal biomarkers. The third specific aims will develop a series of nonlinear functional mixed effect models for curve registration and branching curves, and their joint models with time to event data. Each specific aim includes methods development, theoretical studies, empirical simulations and applications. We will also develop a user-friendly software package that includes all the proposed features and post it to public domain.

描述（由申请人提供）：纵向生物标志物和事件时间数据的半参数联合模型该项目的目标是开发新的统计方法来联合建模纵向生物标志物轨迹和事件时间数据。所提出的方法是有动力的，将应用于三个主要应用：1）通过联合器官共享网络（UNOS）提供的肝移植和肾移植，2）通过美国肾病研究所提供的终末期肾病（ESRD）数据数据系统 (USRDS)，以及 3) 宾夕法尼亚大学收集的先前剖腹产后阴道分娩 (VBAC) 数据。主要动机来自这样一个事实，即生物标志物通常是潜在疾病过程的替代物，在对事件数据的时间进行建模时需要将其视为替代结果，并且生物标志物的轨迹通常需要非参数模型，允许随时间变化的灵活模式，例如平滑曲线、形状记录曲线和分支曲线。另一个动机是，在预测死亡等事件时，生物标志物的累积效应可能比并发值更合适，因此我们建议将功能数据分析和生存分析的思想结合起来。我们将首先开发功能加速失效时间（AFT）模型及其与功能混合效应模型的连接模型。然后，我们扩展该框架以包括非高斯纵向生物标志物。第三个具体目标是开发一系列用于曲线配准和分支曲线的非线性函数混合效应模型，以及它们与事件时间数据的联合模型。每个具体目标包括方法开发、理论研究、实证模拟和应用。我们还将开发一个用户友好的软件包，其中包括所有建议的功能，并将其发布到公共领域。