Association, Regression and Diagnostic Accuracy Analyses of Competing Risks Data

竞争风险通常发生在分析具有复合终点的时间到事件结果时。由于竞赛项目的依赖审查，处理通常的独立审查的标准方法，例如对观察时间进行限制的审查，可能不适用。在本提案中，研究者讨论了两个项目，解决了竞争风险数据分析所带来的挑战。第一个项目旨在利用囊性纤维化基金会的注册数据量化两次肺部感染时间之间的关联，其中事件时间被截断，竞争风险被审查。考虑了条件原因特异性危害函数（CSH）和条件累积关联函数（CIFs）的左截断。提出了一种估计二元条件生存函数的扩展Dabrowska方法，并将其用于估计二元条件CIF。随后进行非参数关联分析，基于关联度量，通过条件累积CSH函数和CIFs进行量化。第二个项目的目标是探索回归设置中CIFs之间的重要内在关系，并提出一个明确考虑CIFs可加性约束的灵活参数回归模型。参数模型采用修正logistic模型作为基准，协变量效应采用广义赔率模型。该模型明确考虑了一个约束条件，即具有任何给定预后因素的受试者最终都可能因其中一个原因而失败，因此ci的渐近线加起来应该等于1。对于双变量竞争风险数据的关联分析研究有限，而对于左截断竞争风险数据的关联分析也没有研究，这是使用注册表数据来量化两个感兴趣事件之间关联的常见情况。基于CIFs的回归模型已经得到了很好的研究，以评估存在竞争风险审查的情况下对感兴趣事件的协变量影响。然而，现有的方法没有明确地考虑到CIFs的可加性约束，导致解释问题。拟议的两个项目分别解决了这些方法上的差距，并有望增进我们对这两个领域的理解。这些拟议的项目是由PI在与其他领域的研究人员合作时遇到的实际问题所推动的，旨在解决这些实际问题。这些项目可应用于医学、公共卫生、工程可靠性研究、精算科学和金融等多个领域。PI正在积极与研究生合作，并希望他们中的一些人能够参与到他们的论文研究中来。因此，建议的工作自然会通过研究生的指导和培训与教育相结合。

Competing risks commonly occur in analyzing time-to-event outcomes with composite endpoints. Due to dependent censoring imposed by competing events, standard methods for dealing with usual independent censoring, such as censoring imposed by time limits on the duration of observation, may not be applicable. In this proposal, the investigator discusses two projects that address challenges arising from the analyses of competing risks data. The first project aims to quantify the association between two lung infection times using the Cystic Fibrosis Foundation registry data, where the event times are left truncated and competing-risk censored. Conditional cause-specific hazard (CSH) functions and conditional cumulative incidence function (CIFs) are considered to incorporate left truncation. An extended Dabrowska method is proposed to estimate the bivariate conditional survival function, and then used to estimate the bivariate conditional CIF. Nonparametric association analysis is subsequently carried out based on association measures that are quantified through conditional cumulative CSH functions and CIFs. The goal of the second project is to explore an important intrinsic relationship between CIFs in a regression setting, and propose a flexible parametric regression model that explicitly takes into account the additivity constraint on the CIFs. The parametric model adopts a modified logistic model as baseline CIFs and a generalized odds-rate model for covariate effects. This model explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes so the asymptotes of the CIFs should add up to one. There is limited research on association analysis of bivariate competing risks data and no prior work for left-truncated competing risks data, a common situation when registry data are used to quantify the association between two events of interest. Regression models based on CIFs have been well studied to evaluate covariate effects on the event of interest in the presence of competing-risk censoring. However, existing methods do not explicitly account for the additivity constraint on CIFs, resulting in interpretation issues. The proposed two projects address each of these methodological gaps and are expected to enhance our understanding of the two areas. The proposed projects have been motivated by real problems that the PI encountered in collaborations with researchers from other areas, and are designed to address those practical issues. The projects can be applied in such diverse fields as medicine, public health, reliability studies in engineering, actuarial sciences and finance. The PI is actively working with graduate students and expects some of them will get involved with the proposed research for their dissertations. Hence the proposed work will be naturally integrated with education through graduate student advising and training.