Hierarchical likelihood inference on clustered competing risks data.
The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause-specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset.
Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, U.S.A.
Nicholas J
NJ
Christian
Department of Statistics, Pukyong National University, Busan, 609-737, Korea.
Il Do
ID
Ha
Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, 15261, U.S.A.
Jong-Hyeon
JH
Jeong
D000465:Algorithms; D056808:Biostatistics / Q000379:methods; D001943:Breast Neoplasms / Q000188:drug therapy; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012044:Regression Analysis; D012306:Risk
cause-specific hazard; clustered data; competing risks; frailty models; hierarchical likelihood