Statistical models for clustered survival data and multivariate recurrent events
聚类生存数据和多变量复发事件的统计模型
基本信息
- 批准号:RGPIN-2014-05977
- 负责人:
- 金额:$ 1.02万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2017
- 资助国家:加拿大
- 起止时间:2017-01-01 至 2018-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In scientific and biomedical research, data being collected may not be independent due to the fact of clustering. For example, in multiple centre clinical trials, patients from the same hospital / clinical centre may share some common risk factors. Fail to take the population heterogeneity into account in the statistical model could lead to biased results and loss of model efficiency. My research plan for this discovery grant is to develop statistical methods for survival and recurrent events data with predictive biomarker variables when individual observations are not independent. In certain types of cancer, such as Hodgkin lymphoma, patients have long life expectancy, and individuals who experience complete remission during the treatment period may have different survival distribution compared to those who do not. It is then important to use the early response data to predict the survival distribution later, and this can be achieved by studying the potential association between the treatment response and survival. I will develop joint models of short term response outcome and long term survival outcome for clustered data. The proposed joint model can be further extended to accommodate different types of outcomes such as continuous variables and counting variables. Predictive biomarkers are patient characteristics that are measured as an indicator of normal biological processes, which can be used to predict which patients will or will not benefit from a new therapy. I will develop new procedures to evaluate biomarker defined subset effect for clustered data. I plan to use flexible statistical models for predictive biomarker in clustered survival data. When the threshold parameter is located near the boundary of the biomarker variable, I will investigate how existing test procedures perform under this circumstance and develop new test for the existence of the biomarker threshold. Recurrent event data occur frequently in many clinical trial and epidemiologic studies. In some situations, the recurrent event may be terminated by a failure event such as death. I will propose multivariate random effects models to investigate the effect of risk factor to multi-type recurrent events in the present of dependent termination and clustering.I will contribute to high quality personnel training by involving different level of students (undergraduate, Master of Sciences and doctoral students) to develop novel statistical methodologies and theories related to this proposal. I will work with M. Sc. Students to develop computational procedures for hypothesis testing problems such as population heterogeneity in joint model, evaluating performance of existing methods for clustered survival and recurrent events data. Together with my Ph. D students, I will investigate new methodologies to make simultaneous statistical inferences for marker response and survival outcomes, develop flexible statistical methods to estimate biomarker-treatment interaction and explore new models for clustered multivariate recurrent events.The proposed research will advance new statistical methodologies and theories to deal with clustered survival data and recurrent event data. These methods can reduce bias of the estimation, improve the efficiency of the statistical model and address the computational challenges for complex data structures. Software developed from the proposed research will benefit statistical science, engineering and reliability research and the biomedical research community in Canada.
在科学和生物医学研究中,由于聚类的事实,收集的数据可能不是独立的。例如,在多个中心的临床试验中,来自同一医院/临床中心的患者可能具有一些共同的风险因素。没有在统计模型中考虑种群的异质性可能会导致结果的偏差和模型效率的损失。我对这笔发现基金的研究计划是,当个体观察不独立时,开发具有预测性生物标记变量的生存和复发事件数据的统计方法。在某些类型的癌症中,如霍奇金淋巴瘤,患者的预期寿命很长,在治疗期内经历完全缓解的人可能与那些没有完全缓解的人有不同的生存分布。因此,重要的是使用早期反应数据来预测以后的生存分布,这可以通过研究治疗反应和生存之间的潜在关联来实现。我将为集群数据开发短期反应结果和长期生存结果的联合模型。提出的联合模型可以进一步扩展,以适应不同类型的结果,如连续变量和计数变量。预测性生物标记物是作为正常生物过程的指标来衡量的患者特征,可用于预测哪些患者将或将不会从新的治疗中受益。我将开发新的程序来评估生物标志物定义的子集对聚集数据的影响。我计划在聚集的生存数据中使用灵活的统计模型来预测生物标记物。当阈值参数位于生物标志物变量的边界附近时,我将调查现有的测试程序在这种情况下是如何执行的,并开发新的生物标志物阈值存在的测试。复发事件数据经常出现在许多临床试验和流行病学研究中。在某些情况下,重复事件可能由故障事件(例如死亡)终止。我将提出多元随机效应模型,以探讨在依赖终止和聚集的情况下,危险因素对多类型复发事件的影响。我将通过让不同层次的学生(本科生、硕士和博士生)参与进来,发展与这一建议相关的新的统计方法和理论,为高质量的人才培养做出贡献。我会和M.SC一起工作。学生开发假设检验问题的计算程序,如联合模型中的种群异质性、评估现有方法的性能、集群生存和复发事件数据。与我的博士生一起,我将研究新的方法来同时对标记物的反应和生存结果进行统计推断,开发灵活的统计方法来估计生物标记物与治疗的相互作用,并探索聚集性多变量复发事件的新模型。这些方法可以减少估计的偏差,提高统计模型的效率,解决复杂数据结构的计算挑战。根据拟议的研究开发的软件将使统计科学、工程和可靠性研究以及加拿大的生物医学研究界受益。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Chen, Bingshu其他文献
Vitamin D Levels, Vitamin D Receptor Polymorphisms, and Inflammatory Cytokines in Aromatase Inhibitor-Induced Arthralgias: An Analysis of CCTG MA.27
- DOI:
10.1016/j.clbc.2017.10.009 - 发表时间:
2018-02-01 - 期刊:
- 影响因子:3.1
- 作者:
Niravath, Polly;Chen, Bingshu;Ingle, James N. - 通讯作者:
Ingle, James N.
Role of Cytotoxic Tumor-Infiltrating Lymphocytes in Predicting Outcomes in Metastatic HER2-Positive Breast Cancer A Secondary Analysis of a Randomized Clinical Trial
- DOI:
10.1001/jamaoncol.2017.2085 - 发表时间:
2017-11-01 - 期刊:
- 影响因子:28.4
- 作者:
Liu, Shuzhen;Chen, Bingshu;Nielsen, Torsten O. - 通讯作者:
Nielsen, Torsten O.
Chen, Bingshu的其他文献
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{{ truncateString('Chen, Bingshu', 18)}}的其他基金
Statistical infernce for survival data: nonparametric methods and deep learning
生存数据的统计推断:非参数方法和深度学习
- 批准号:
RGPIN-2019-05574 - 财政年份:2022
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical infernce for survival data: nonparametric methods and deep learning
生存数据的统计推断:非参数方法和深度学习
- 批准号:
RGPIN-2019-05574 - 财政年份:2021
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical infernce for survival data: nonparametric methods and deep learning
生存数据的统计推断:非参数方法和深度学习
- 批准号:
RGPIN-2019-05574 - 财政年份:2020
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical infernce for survival data: nonparametric methods and deep learning
生存数据的统计推断:非参数方法和深度学习
- 批准号:
RGPIN-2019-05574 - 财政年份:2019
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical models for clustered survival data and multivariate recurrent events
聚类生存数据和多变量复发事件的统计模型
- 批准号:
RGPIN-2014-05977 - 财政年份:2018
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical models for clustered survival data and multivariate recurrent events
聚类生存数据和多变量复发事件的统计模型
- 批准号:
RGPIN-2014-05977 - 财政年份:2016
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical models for clustered survival data and multivariate recurrent events
聚类生存数据和多变量复发事件的统计模型
- 批准号:
RGPIN-2014-05977 - 财政年份:2015
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
Statistical models for clustered survival data and multivariate recurrent events
聚类生存数据和多变量复发事件的统计模型
- 批准号:
RGPIN-2014-05977 - 财政年份:2014
- 资助金额:
$ 1.02万 - 项目类别:
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Statistical methods in clinical trials and epidemiology studies
临床试验和流行病学研究中的统计方法
- 批准号:
371398-2009 - 财政年份:2013
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
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临床试验和流行病学研究中的统计方法
- 批准号:
371398-2009 - 财政年份:2012
- 资助金额:
$ 1.02万 - 项目类别:
Discovery Grants Program - Individual
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