Association, Regression and Diagnostic Accuracy Analyses of Competing Risks Data

在分析具有复合终点的至事件发生时间结局时，通常会出现竞争风险。由于竞争事件造成的相关删失，处理通常的独立删失的标准方法，例如观察持续时间的时间限制造成的删失，可能不适用。在本提案中，研究者讨论了两个项目，这些项目解决了竞争性风险数据分析中出现的挑战。第一个项目旨在使用囊性纤维化基金会登记数据量化两个肺部感染时间之间的关联，其中事件时间被截断并进行竞争风险审查。条件原因特异性危害（CSH）函数和条件累积发生率函数（CIF）被认为是纳入左截断。提出了一种扩展的Dabrowska方法来估计二元条件生存函数，并将其应用于二元条件CIF的估计。随后进行非参数关联分析的基础上，通过条件累积CSH函数和CIF量化的关联措施。第二个项目的目标是探索一个重要的内在关系的CIF在回归设置，并提出了一个灵活的参数回归模型，明确考虑到加性约束的CIF。参数模型采用改良的logistic模型作为基线CIF，协变量效应采用广义比值率模型。该模型明确考虑了具有任何给定预后因素的受试者最终应因其中一个原因而失败的约束，因此CIF的渐近线应加起来为1。关于双变量竞争风险数据的关联分析的研究有限，并且之前没有针对左截断竞争风险数据的工作，这是使用登记数据量化两个关注事件之间关联的常见情况。已对基于CIF的回归模型进行了充分研究，以评价在竞争风险删失的情况下协变量对关注事件的影响。然而，现有的方法并没有明确地考虑到加和性约束的CIF，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，预计将加强我们对这两个领域的了解。拟议的项目是由PI与其他领域的研究人员合作遇到的真实的问题激发的，旨在解决这些实际问题。这些项目可以应用于医学、公共卫生、工程可靠性研究、精算科学和金融等不同领域。PI正在积极与研究生合作，并希望他们中的一些人将参与他们的论文的拟议研究。因此，拟议中的工作将通过研究生咨询和培训自然地与教育相结合。

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.