Location: CW307.20
Speaker: Xuewen Lu, University of Calgary
Title: Variable Selection in Joint Frailty Model of Recurrent and Terminal Events with Diverging Number of Covariates
Abstract:
In many biomedical applications, the recurrent event data are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted many research interests, however, very few works have been done for simultaneous estimation and variable selection for joint frailty proportional hazards models, moreover, it is lacking a theoretical justification and a validity when the dimension of covariates is diverging with the sample size. To fill this gap, we propose a broken adaptive ridge (BAR) regression procedure that combines the strengths of the quadratic regularization and the adaptive weighted bridge shrinkage. We establish the oracle property of the BAR regression. In the simulation study, the results indicate that the BAR regression outperforms the existing variable selection methods. Finally, the proposed method is applied to a real dataset for illustration.