Statistical Methodologies for Competing Risks

竞争风险的统计方法

• 批准号: RGPIN-2014-06157
• 负责人: Choi, YunHee
• 金额: $ 0.8万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635932
• 关键词: Statistical; Methodologies; Competing; Risks

Competing risks often arise in the analysis of survival data in biomedical research. The primary objective of this research program is to develop appropriate statistical methodology for clustered data with multiple events in the presence of competing risks. I propose to develop a progressive competing risks model as an extension of a classical competing risks model where at each stage individuals can experience different events. This model will be further developed to appropriately take into account the complex correlation structures. Specifically, I have four aims for this research: (1) to develop a general methodology for modeling successive events in the presence of competing risks; (2) to handle complex dependence structures; (3) to incorporate time-dependent or interval-censored covariates; (4) to evaluate the predictive performance of the model.Aim (1): Establish a progressive model for successive events occurring in multiple-stage disease processes for correlated survival data in the presence of competing risks. We will develop a progressive competing risk model using two commonly used approaches based on the cause-specific hazard and subdistribution hazard regressions. We will then evaluate the impact of competing events on the absolute risks of developing the event of interest at each event stage.Aim (2): Develop statistical approaches for handling complex dependence structures occurring due to the correlation among multiple events within individuals and the correlation among family members. Nested frailty models can be adopted to accommodate the hierarchical dependencies based on individual-specific and family-specific random variables. In addition, Copula models will be adopted for joining marginal distributions of multiple outcomes while nested frailty models will take into account complex familial correlations. Aim (3): Develop statistical methods for time-dependent or interval-censored covariates for the proposed models. Interval-censored data arise in longitudinal studies where the time to an event is not directly observed but is known only to have occurred within an interval of time. We will develop estimation procedures to handle interval-censored data, especially to incorporate time-dependent or interval-censored covariates into the proposed model to examine their effects at different stages of multiple disease processes.Aim (4): Develop predictive measures for the risk of developing a future event in the presence of competing risks along the disease process. We will derive several predictive measures such as cumulative incidence functions and transition probabilities based on the model proposed in Aim 1. In addition, we will derive a dynamic cumulative function that estimates the conditional probability of developing an event of interest within a fixed window of time (e.g., 5-year prediction). For statistical inference, we will further develop the robust variance estimates and confidence intervals for comparing cumulative incidence curves or dynamic prediction curves at different covariate sets. Based on the proposed predictive measures, we will evaluate and compare the predictive performance of the model. We believe that the proposed development and evaluation of statistical methodologies for multiple event processes obtained from correlated survival data in the presence of competing risks will have important applications to genetic and biomedical research problems, in particular, to elucidate the genetic basis of many complex diseases.

在生物医学研究的生存数据分析中，经常会出现相互竞争的风险。该研究计划的主要目标是在存在竞争风险的情况下，为具有多个事件的集群数据开发适当的统计方法。我建议开发一个渐进竞争风险模型，作为经典竞争风险模型的扩展，在每个阶段，个人可以经历不同的事件。这一模型将进一步发展，以适当考虑到复杂的相关结构。具体地说，我对这项研究有四个目标：(1)开发一个在存在竞争风险的情况下对连续事件进行建模的一般方法；(2)处理复杂的相关性结构；(3)纳入时间依赖或区间删失的协变量；(4)评估模型的预测性能。目的(1)：为存在竞争风险的多阶段疾病过程中发生的连续事件建立一个累进模型，以获得相关的生存数据。我们将使用两种常用的方法开发一个渐进竞争风险模型，该模型基于特定原因的风险和次分布风险回归。然后，我们将评估竞争事件对每个事件阶段发生感兴趣事件的绝对风险的影响。目的(2)：开发统计方法来处理由于个人内部多个事件之间的相关性以及家庭成员之间的相关性而发生的复杂依赖结构。可以采用嵌套脆弱模型来适应基于特定于个人和特定于家庭的随机变量的分层依赖关系。此外，Copula模型将用于连接多个结果的边缘分布，而嵌套脆弱性模型将考虑复杂的家族相关性。目的(3)：为所提出的模型发展时间相关或区间删失协变量的统计方法。区间删失数据出现在纵向研究中，其中到事件的时间不是直接观察到的，而是已知仅在一段时间间隔内发生的。我们将开发估计程序来处理区间删失数据，特别是将时间依赖或区间删失协变量纳入建议的模型中，以检查它们在多个疾病过程的不同阶段的影响。目的(4)：在疾病过程中存在竞争风险的情况下，制定未来事件发生的风险预测指标。我们将根据目标1中提出的模型推导出几个预测指标，例如累积关联函数和转移概率。此外，我们还将推导出一个动态累积函数，该函数估计在固定时间窗口内发生感兴趣事件的条件概率(例如，5年预测)。对于统计推断，我们将进一步开发稳健的方差估计和可信区间，以比较不同协变量集上的累积关联曲线或动态预测曲线。基于所提出的预测指标，我们将对模型的预测性能进行评估和比较。我们相信，在存在竞争风险的情况下，从相关的生存数据中获得的多事件过程的统计方法的拟议发展和评估将在遗传和生物医学研究问题上有重要的应用，特别是在阐明许多复杂疾病的遗传基础方面。