Copula-Based Methods for Multivariate Survival Analysis

非致命性疾病相关的困难降低了生活质量。例如，癌症幸存者经常因治疗或疾病进展而经历不利的健康结果。了解时间对这些事件的依赖性对于设计有效的护理至关重要。该项目将开发新的统计方法来模拟这些依赖关系，以及这些非致命事件的发生如何影响总体生存。这项研究的结果可以为新的治疗策略提供信息，并改善许多患者的生活质量。本项目将研究与课程开发和研究生培养相结合，为本科生和高中生提供研究机会。该项目利用copulas（一种流行的统计工具，用于对事件时间之间的依赖性进行建模）来实现三个主要目标。第一个目标是开发一种基于似然的拟合优度检验，用于检测可能导致无效估计的联结错配。第二个目标侧重于审查导致的事件缺失时间，例如终止后续。将设计新的过程，通过事件间依赖来计算缺失的事件时间。第三个目标是建立一个藤联结框架来模拟时间对多个非致命事件的依赖性及其与死亡时间的关系。这项研究工作的理论方面将通过在R中开发供研究社区使用的开源软件包来补充。该项目由统计计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Nonfatal disease-related hardship diminish quality of life. For example, cancer survivors often experience adverse health outcomes resulting from treatment or disease progression. Understanding the dependence among times to such events is crucial for designing effective care. This project will develop new statistical methods to model such dependencies and how the occurrence of these nonfatal events affects overall survival. The results of this research can inform new treatment strategies and improve the quality of life for many patients. This project will integrate the research with curriculum development and the training of graduate students and provide research opportunities for undergraduate and high school students.The project leverages copulas, popular statistical tools for modeling the dependence among event times, to address three major aims. The first aim is to develop a likelihood-based goodness-of-fit test for detecting copula misspecification, which would lead to invalid estimation. The second aim focuses on missing event times caused by censoring, such as the termination of follow-up. New procedures will be designed to impute the missing event times via inter-event dependence. The third aim is to establish a vine copula framework to model the dependence among times to multiple nonfatal events and their relationship with time to death. The theoretical aspects of this research effort will be complemented by developing open-source software packages in R for use by the research community. This project is jointly funded by the Statistics Program and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.