Statistical Methodologies for Competing Risks

点击翻译按钮获取中文摘要

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.