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

IL DO HA

Abstract:

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, andHL. 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 SCADmethodswithout losing prediction
accuracy. The proposed method is illustrated by using two kinds of clustered
competing-risks cancer data sets.