title: A new approach to regression analysis of censored competing-risks data.
abstract: An approximate likelihood approach is developed for regression analysis of censored competing-risks data. This approach models directly the cumulative incidence function, instead of the cause-specific hazard function, in terms of explanatory covariates under a proportional subdistribution hazards assumption. It uses a self-consistent iterative procedure to maximize an approximate semiparametric likelihood function, leading to an asymptotically normal and efficient estimator of the vector of regression parameters. Simulation studies demonstrate its advantages over previous methods.
authors: Quantitative Marketing, Google, New York, NY, 10011, USA. yuxue@google.com.
authors: Yuxue
authors: Y
authors: Jin
authors: Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
authors: Tze Leung
authors: TL
authors: Lai
mesh_terms: D016026:Bone Marrow Transplantation; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007938:Leukemia / Q000401:mortality / Q000628:therapy; D015993:Life Tables; D016013:Likelihood Functions; D015233:Models, Statistical; D016011:Normal Distribution; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012306:Risk
keywords: Asymptotic efficiency; Cumulative incidence function; Empirical process theory; Hazard function of subdistribution; Martingale central limit theorem; Semiparametric likelihood; Volterra equation