Penalized variable selection for cause‐specific hazard frailty models with clustered competing‐risks data

• DOI: 10.1002/sim.9197
• Publication Date: 2021-09-20T07:52:14Z
• Authors: Trias W. Rakhmawati; Il Do Ha; Hangbin Lee; Youngjo Lee
• Keywords: Likelihood Functions; Models, Statistical; Frailty; Humans; Computer Simulation; 0101 mathematics; 01 natural sciences; Proportional Hazards Models; 3. Good health

Competing risks data usually arise when an occurrence of an event precludes other types of events from being observed. Such data are often encountered in a clustered clinical study such as a multi‐center clinical trial. For the clustered competing‐risks data which are correlated within a cluster, competing‐risks models allowing for frailty terms have been recently studied. To the best of our knowledge, however, there is no literature on variable selection methods for cause‐specific hazard frailty models. In this article, we propose a variable selection procedure for fixed effects in cause‐specific competing risks frailty models using a penalized h‐likelihood (HL). Here, we study three penalty functions, LASSO, SCAD, and HL. Simulation studies demonstrate that the proposed procedure using the HL penalty works well, providing a higher probability of choosing the true model than LASSO and SCAD methods without losing prediction accuracy. The proposed method is illustrated by using two kinds of clustered competing‐risks cancer data sets.