Copula-Based Methods for Multivariate Survival Analysis

与非致命性疾病相关的困难会降低生活质量。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后果。了解不同时间对此类事件的依赖关系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种依赖关系，以及这些非致命事件的发生如何影响总体存活率。这项研究的结果可以为新的治疗策略提供参考，并改善许多患者的生活质量。该项目将把研究与课程开发和研究生培养相结合，并为本科生和高中生提供研究机会。该项目利用Copulas这一流行的统计工具对事件时间之间的相关性进行建模，以解决三个主要目标。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Copula错误指定。第二个目标集中在审查造成的错过事件时间，例如终止跟踪。将设计新的程序，通过事件间相关性来计算丢失的事件时间。第三个目标是建立一个Vine Copula框架来模拟时间之间对多个非致命性事件的依赖以及它们与死亡时间的关系。这项研究工作的理论方面将通过开发供研究界使用的R开源软件包来补充。该项目由统计计划和既定的激励竞争性研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.