  The aim of this article is to analyze data from multiple repairable systems
under the presence of dependent competing risks. In order to model this
dependence structure, we adopted the well-known shared frailty model. This
model provides a suitable theoretical basis for generating dependence between
the components failure times in the dependent competing risks model. It is
known that the dependence effect in this scenario influences the estimates of
the model parameters. Hence, under the assumption that the cause-specific
intensities follow a PLP, we propose a frailty-induced dependence approach to
incorporate the dependence among the cause-specific recurrent processes.
Moreover, the misspecification of the frailty distribution may lead to errors
when estimating the parameters of interest. Because of this, we considered a
Bayesian nonparametric approach to model the frailty density in order to offer
more flexibility and to provide consistent estimates for the PLP model, as well
as insights about heterogeneity among the systems. Both simulation studies and
real case studies are provided to illustrate the proposed approaches and
demonstrate their validity.
