Association, Regression and Diagnostic Accuracy Analyses of Competing Risks Data

竞争风险数据的关联、回归和诊断准确性分析

• 批准号: 1207711
• 负责人: Yu Cheng
• 金额: $ 9.99万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1207711&HistoricalAwards=false
• 关键词: Association; Regression; Diagnostic; Accuracy; Analyses

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.

竞争风险通常发生在使用复合端点分析事件间隔时间结果时。由于相互竞争的事件强加的独立审查，处理通常的独立审查的标准方法，例如由观察时间限制强加的审查，可能不适用。在这份提案中，调查员讨论了两个项目，这两个项目应对竞争风险数据分析所产生的挑战。第一个项目旨在使用囊性纤维化基金会的注册数据来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。条件原因特定危险(CSH)函数和条件累积关联函数(CIF)被认为包含了左截断。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。随后基于通过条件累积CSH函数和CIF量化的关联度量来执行非参数关联分析。第二个项目的目标是探索回归环境中CIF之间的重要内在关系，并提出一个灵活的参数回归模型，该模型明确考虑了CIF的可加性约束。参数模型采用改进的Logistic模型作为基准CIF，并采用广义优势比模型进行协变量效应分析。这个模型明确考虑了这样一个约束，即具有任何给定预后因素的受试者最终应该因其中一个原因而失败，因此CIF的渐近线应该加在一起为1。关于双变量竞争风险数据的关联分析的研究有限，对左截断竞争风险数据的先前工作没有，这是使用登记册数据来量化两个感兴趣的事件之间的关联的常见情况。基于CIF的回归模型已经被很好地研究，以评估在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，现有方法没有明确地考虑对CIF的可加性限制，从而导致解释问题。拟议的两个项目分别解决了这些方法上的差距，预计将增进我们对这两个领域的了解。提议的项目的动机是国际和平研究所在与其他领域的研究人员合作时遇到的实际问题，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正在积极地与研究生合作，并希望他们中的一些人参与到他们的论文中拟议的研究中。因此，拟议的工作将通过研究生咨询和培训自然地与教育相结合。