Penalized weigted competing risks models based on quantile regression

The proportional subdistribution hazards (PSH) model is popularly used to deal with competing risks data. Censored quantile regression provides an important supplement as well as variable selection methods, due to large numbers of irrelevant covariates in practice. In this paper, we study variable selection procedures based on penalized weighted quantile regression for competing risks models, which is conveniently applied by researchers. Asymptotic properties of the proposed estimators including consistency and asymptotic normality of non-penalized estimator and consistency of variable selection are established. Monte Carlo simulation studies are conducted, showing that the proposed methods are considerably stable and efficient. A real data about bone marrow transplant (BMT) is also analyzed to illustrate the application of proposed procedure.