An eigenfunction approach to multivariate and high-dimensional survival analysis

多变量和高维生存分析的特征函数方法

• 批准号: RGPIN-2016-05722
• 负责人: Sen, Arusharka
• 金额: $ 1.09万
• 依托单位: Concordia University
• 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: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636019
• 关键词: eigenfunction; approach; multivariate; dimensional; survival

Survival analysis is the study of so-called time-to-event data, and typically multivariate survival data arise either as parallel data, where one tries to observe times to events of interest (such as disease onset, death, failure) for several related individuals (such as family members, organs of human body, components of a system), or serial data, where a series of successive events are tracked (such as stages of a disease), or possibly as a combination of these two schemes. Often time to event data is combined with high-dimensional covariates, such as information on genetic markers available from gene expression data. Two major sources of complication in time to event data are

生存分析是对所谓的事件发生时间数据的研究，通常多变量生存数据作为平行数据出现，其中人们试图观察感兴趣的事件发生时间（如发病、死亡、失败）（例如家庭成员、人体器官、系统组件）或串行数据，其中跟踪一系列连续事件（例如疾病的阶段），或者可能作为这两种方案的组合。通常，事件发生时间数据与高维协变量相结合，例如从基因表达数据中获得的遗传标记信息。事件发生时间数据的两个主要并发症来源是