  We consider variable selection in competing risks regression for multi-center
data. Our research is motivated by deceased donor kidney transplants, from
which recipients would experience graft failure, death with functioning graft
(DWFG), or graft survival. The occurrence of DWFG precludes graft failure from
happening and therefore is a competing risk. Data within a transplant center
may be correlated due to a latent center effect, such as varying patient
populations, surgical techniques, and patient management. The proportional
subdistribution hazard (PSH) model has been frequently used in the regression
analysis of competing risks data. Two of its extensions, the stratified and the
marginal PSH models, can be applied to multi-center data to account for the
center effect. In this paper, we propose penalization strategies for the two
models, primarily to select important variables and estimate their effects
whereas correlations within centers serve as a nuisance. Simulations
demonstrate good performance and computational efficiency for the proposed
methods. It is further assessed using an analysis of data from the United
Network of Organ Sharing.
