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分析复合端点的事件时间结果时。由于竞争事件对竞争事件施加的依赖进行了审查，因此可能不适用于处理通常的独立审查的标准方法，例如在观察持续时间内对时间限制施加的检查，可能不适用。在此提案中，研究人员讨论了两个项目，这些项目解决了竞争风险数据的分析引起的挑战。第一个项目旨在使用囊性纤维化基金会注册表数据量化两个肺部感染时间之间的关联，在该数据中，事件时间被截断并竞争风险审查。有条件原因特异性危害（CSH）功能和条件累积发病率函数（CIF）被认为包含左截断。提出了一种扩展的Dabrowska方法来估计双变量条件存活函数，然后用于估计双变量条件CIF。随后，非参数关联分析是根据通过条件累积CSH函数和CIF量化的关联度量进行的。第二个项目的目的是探索回归环境中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CIFS上的添加性约束。参数模型采用修改后的逻辑模型作为基线CIF和协变量效应的广义赔率率模型。该模型明确考虑了以下约束，即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，因此CIF的渐近线应加起来。关于双变量竞争风险数据的关联分析研究的研究有限，没有用于左截断的竞争风险数据的工作，这是一种常见情况，当使用注册表数据来量化两个感兴趣的事件之间的关联。已经对基于CIF的回归模型进行了充分的研究，以评估在存在竞争风险审查的情况下对感兴趣的事件的协方差影响。但是，现有方法不能明确说明CIF的添加性约束，从而导致解释问题。拟议的两个项目解决了这些方法上的差距，并有望增强我们对这两个领域的理解。拟议的项目是由PI与其他领域的研究人员合作遇到的实际问题的动机，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，精算科学和金融方面的可靠性研究等各种领域。 PI正在与研究生积极合作，并希望其中一些人将参与提交论文的拟议研究。因此，拟议的工作将通过研究生建议和培训自然地与教育融合。