Method Development for Survival Dynamic Regression in Chronic Disease Research

慢性病研究中生存动态回归的方法开发

基本信息

  • 批准号:
    8686941
  • 负责人:
  • 金额:
    $ 30.51万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-08-06 至 2017-06-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Understanding the association between risk factors and chronic diseases is crucially important for improvement in treatment and patient care. Population based data registries for chronic disease are often available and provide excellent platforms to serve this purpose. A crucial aspect of traditional regression modeling is the assumption that the effect of each risk factor is constant. However, there is growing evidence that the etiology of many chronic diseases is complex and the influence of a risk factor on disease outcomes may not be constant. Through our ongoing collaborative work on Cystic Fibrosis Foundation patient registry (CFFPR), the investigators of this grant have demonstrated that varying coefficient regression, termed as dynamic regression in survival analysis (Martinussen and Scheike, 2006), provides a powerful tool for discovering important changes in associations between risk factors and CF outcomes (time to, and frequency of, major CF events). This research project is motivated by several important unsolved, open questions arising from CFFPR: (i) competing risk, double censoring and left truncation to the observation of CF events, inherited with the design of CFFPR; (ii) the high dimensional covariates, which necessitate the development of variable selection procedures that accommodate varying covariate effects, (iii) the large sample size, which demands efficient computation; (iv) the need of utilizing time-dependent follow-up information to aid in disease prognosis and address substantive scientific questions. Current dynamic regression approaches have several limitations: inability to accommodate complex features of data, difficulties in interpretation and prediction, computational issues. Moreover, there is very limited work on variable selection under survival varying coefficient models. The overall objective of this proposal is to develop a comprehensive dynamic regression framework that resolves the key limitations of the existing approaches and possesses the capacity to handle many realistic data-related issues. To accomplish this goal, we will first lay out a unified framework of survival dynamic regression by introducing sensible modeling and developing inferential procedures that account for common survival data features (Aim 1). We will tackle the challenging problem of high dimensional dynamic regression (Aim 2), where the existing methods that assume constant effects can have poor performance. We will propose a seminal dynamic regression strategy for investigating the relationship between time-dependent covariates and survival outcomes with sensible interpretations and predictions permitted (Aim 3). The proposed statistical methods will be applied to CFFPR (Aim 4) and user-friendly software will be develop and made available to general research communities (Aim 5). Methodological development proposed in this grant will have a broad impact on scientific investigations not only on CFFPR but also on other registry based chronic disease studies.
描述(由申请人提供):了解风险因素与慢性疾病之间的关联对于改善治疗和患者护理至关重要。基于人群的慢性病数据登记通常是可用的,并为实现这一目的提供了极好的平台。传统回归建模的一个重要方面是假设每个风险因素的影响是恒定的。然而,越来越多的证据表明,许多慢性疾病的病因是复杂的,风险因素对疾病结局的影响可能不是恒定的。通过我们正在进行的囊性纤维化基金会患者登记(CFFPR)的合作工作,该基金的研究人员已经证明,变系数回归,称为生存分析中的动态回归(Martinussen和Scheike,2006),为发现风险因素与CF结局(主要CF事件的时间和频率)之间相关性的重要变化提供了强大的工具。本研究项目的动机是由几个重要的未解决的,开放的问题所产生的CFFPR:(i)竞争风险,双重删失和左截断CF事件的观察,继承了CFFPR的设计;(ii)高维协变量,这需要发展的变量选择程序,以适应不同的协变量的影响,(iii)大样本量,这需要有效的计算;(iv)需要利用随时间变化的后续信息来帮助疾病预后和解决实质性的科学问题。目前的动态回归方法有几个局限性:无法适应复杂的数据特征,难以解释和预测,计算问题。此外,有非常有限的工作,在生存变系数模型下的变量选择。 本提案的总体目标是制定一个全面的动态回归框架,解决现有方法的主要局限性,并有能力处理许多与数据有关的现实问题。为了实现这一目标,我们将首先通过引入合理的建模和开发解释常见生存数据特征的推理程序来构建生存动态回归的统一框架(目标1)。我们将解决具有挑战性的高维动态回归问题(目标2),现有的方法,假设恒定的影响可能会有很差的性能。我们将提出一个开创性的动态回归策略,用于研究时间依赖性协变量与生存结局之间的关系,并允许合理的解释和预测(目标3)。拟议的统计方法将应用于CFFPR(目标4),并将开发方便用户的软件,提供给一般研究界(目标5)。本补助金中提出的方法学发展将对科学研究产生广泛影响,不仅对CFFPR,而且对其他基于登记的慢性病研究也有影响。

项目成果

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Limin Peng其他文献

Limin Peng的其他文献

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{{ truncateString('Limin Peng', 18)}}的其他基金

Method Development for Survival Dynamic Regression in Chronic Disease Research
慢性病研究中生存动态回归的方法开发
  • 批准号:
    9920015
  • 财政年份:
    2012
  • 资助金额:
    $ 30.51万
  • 项目类别:
Method Development for Survival Dynamic Regression in Chronic Disease Research
慢性病研究中生存动态回归的方法开发
  • 批准号:
    8522227
  • 财政年份:
    2012
  • 资助金额:
    $ 30.51万
  • 项目类别:
Method Development for Survival Dynamic Regression in Chronic Disease Research
慢性病研究中生存动态回归的方法开发
  • 批准号:
    8399568
  • 财政年份:
    2012
  • 资助金额:
    $ 30.51万
  • 项目类别:
Method Development for Survival Dynamic Regression in Chronic Disease Research
慢性病研究中生存动态回归的方法开发
  • 批准号:
    9095468
  • 财政年份:
    2012
  • 资助金额:
    $ 30.51万
  • 项目类别:

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Impact of a Community Care Intervention to Address Inequities in COVID-19 and Influenza Vaccination Among Adults Suffering from Disproportionate Chronic Disease
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Impact of a Community Care Intervention to Address Inequities in COVID-19 and Influenza Vaccination Among Adults Suffering from Disproportionate Chronic Disease
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Photo-enhanced messaging to address dietary sugars as a common risk factor for chronic disease
光增强信息传递解决膳食糖作为慢性病的常见危险因素
  • 批准号:
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建立阿尔伯塔省/不列颠哥伦比亚省研究团队来解决慢性病管理的研究议程
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  • 财政年份:
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    Miscellaneous Programs
Address socio-economic variations in the implementation of guidelines for preventive activities in chronic disease in general practice
在一般实践中解决慢性病预防活动指南实施中的社会经济差异
  • 批准号:
    nhmrc : 1012837
  • 财政年份:
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  • 资助金额:
    $ 30.51万
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该慢性病合作项目旨在解决与慢性病相关的风险因素
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A Systems Framed Synthesis of Recommended Actions to Address Obesity and Chronic Disease Prevention
解决肥胖和慢性病预防问题的建议行动的系统框架综合
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THIS COLLABORATIVE CHRONIC DISEASE PROJECT ADDRESS RISK FASCTORS ASSOCIATED WITH
该慢性病合作项目旨在解决与慢性病相关的风险因素
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