Generalized Semiparametric Regression with Longitudinal Data

纵向数据的广义半参数回归

• 批准号: 1208978
• 负责人: Yanqing Sun
• 金额: $ 12万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208978&HistoricalAwards=false
• 关键词: Generalized; Semiparametric; Regression; Longitudinal; Data

This proposal explores the generalized semiparametric regression model (GSRM) for longitudinal data. The GSRM model allows the effects of some covariates to be constant and others to be time-varying. The model is an extension of the generalized linear model for cross-sectional data. Different link functions can be selected to provide a rich family of models for longitudinal data. Both categorical and continuous longitudinal responses can be modeled with appropriately chosen link functions. Statistical analysis of longitudinal data often involves modeling treatment effects on clinically relevant longitudinal biomarkers since an initial event (the time origin). The proposed research includes two parts with important applications. In the first part, the investigator proposes to examine the GSRM model when the time origin is observed for all subjects. In the second part, the exact time origin may be unknown. The GSRM model provides a big platform for model building and variable selection. The investigator proposes a sampling adjusted profile local linear estimation approach. The nonparametric components of the model will be estimated using the local linear estimating equations and the parametric components are to be estimated through weighted profile estimating functions. In the situation where the exact time origin may be unknown, an EM procedure based on the missing data principle will be investigated. The proposed method will automatically adjust for heterogeneity of sampling times, allowing the sampling strategy to depend on the past sampling history as well as possibly time-dependent covariates without specifically modeling such dependence. Many important issues will be investigated, including variance estimation, hypothesis testing of covariate effects, weight function and bandwidth selections, and goodness of fit. The estimation and hypothesis testing of the link function will also be investigated. The proposed research will be applied to real examples from AIDS clinical trials and vaccine efficacy trials.Longitudinal data are common in medical and public health research. Statistical analysis of longitudinal data often involves modeling treatment effects on clinically relevant longitudinal biomarkers since an initial event. The proposed research investigates a unified approach to the generalized semiparametric regression model for longitudinal data for both the situations where the time of the initial event is known and where the exact time of the initial event may be censored. The proposed research is motivated by real problems in AIDS clinical trials and HIV vaccine efficacy trials. By pursuing the directions outlined in the proposal, significant progress could be made in building biologically interpretable models and in developing statistically efficient methods to deal with the complexity of longitudinal data. The proposed research will contribute to efforts to overcome the medical and public health challenges facing the world today.

本文探讨了纵向数据的广义半参数回归模型（GSRM）。GSRM模型允许某些协变量的影响保持不变，而其他协变量的影响随时间变化。该模型是横截面数据的广义线性模型的扩展。可以选择不同的链接函数，为纵向数据提供丰富的模型系列。分类和连续的纵向响应可以建模与适当选择的链接功能。 纵向数据的统计分析通常涉及自初始事件（时间原点）以来对临床相关纵向生物标志物的治疗效果建模。该研究包括两个具有重要应用价值的部分。在第一部分中，研究者建议在观察所有受试者的时间原点时检查GSRM模型。 在第二部分，确切的时间起源可能是未知的。 GSRM模型为建模和变量选择提供了一个大平台。研究者提出了一种采样调整的局部线性估计方法。将使用局部线性估计方程估计模型的非参数分量，并通过加权轮廓估计函数估计参数分量。 在确切的时间原点可能是未知的情况下，将研究基于缺失数据原理的EM程序。 所提出的方法将自动调整采样时间的异质性，允许采样策略依赖于过去的采样历史以及可能的时间依赖性协变量，而无需专门建模这种依赖性。许多重要的问题将被调查，包括方差估计，协变量效应的假设检验，权重函数和带宽的选择，以及拟合优度。也将研究的估计和假设检验的链接函数。这项研究将应用于艾滋病临床试验和疫苗有效性试验的真实的例子。纵向数据在医学和公共卫生研究中很常见。纵向数据的统计分析通常涉及自初始事件以来对临床相关纵向生物标志物的治疗效果建模。建议的研究探讨了一个统一的方法，广义半参数回归模型的纵向数据的情况下，初始事件的时间是已知的，初始事件的确切时间可能会被删失。这项研究的动机是艾滋病临床试验和艾滋病毒疫苗有效性试验中的真实的问题。按照该提案中概述的方向，可以在建立生物学上可解释的模型和制定统计上有效的方法来处理纵向数据的复杂性方面取得重大进展。拟议的研究将有助于克服当今世界面临的医疗和公共卫生挑战。

项目成果

Weighted estimating equations for additive hazards models with missing covariates

缺少协变量的加性危险模型的加权估计方程

• DOI: 10.1007/s10463-018-0648-y
• 发表时间: 2019
• 期刊: Annals of the Institute of Statistical Mathematics
• 影响因子: 1
• 作者: Qi, Lihong;Zhang, Xu;Sun, Yanqing;Wang, Lu;Zhao, Yichuan
• 通讯作者: Zhao, Yichuan