Penalized Variable Selection for Joint AFT Random-Effect Model With Clustered Competing-Risks Data

Clustered competing-risks data often arise in clinical studies, such as multi-center clinical trials, where the occurrence of an event within a cluster hinders the observation of other types of events. The correlation resulting from clustering can be modeled using random effects. These competing-risks data have usually been analyzed using hazard-based models, rather than survival times themselves. Hao et al. proposed a cause-specific joint accelerated failure time (AFT) random-effect modeling approach for analyzing the clustered competing-risks data, which is easy to interpret. In this article, we propose a variable selection method for fixed effects using a penalized h-likelihood (HL) procedure in the joint AFT competing-risk model. Simulation studies were conducted to evaluate the performance of the proposed variable selection procedure, which concluded that the penalized methods of SCAD and HL are more appropriate than that of LASSO. The proposed method is illustrated with two real clinical datasets.