  One of the commonly used approaches to capture dependence in multivariate
survival data is through the frailty variables. The identifiability issues
should be carefully investigated while modeling multivariate survival with or
without competing risks. The use of non-parametric frailty distribution(s) is
sometimes preferred for its robustness and flexibility properties. In this
paper, we consider modeling of bivariate survival data with competing risks
through four different kinds of non-parametric frailty and parametric baseline
cause-specific hazard functions to investigate the corresponding model
identifiability. We make the common assumption of the frailty mean being equal
to unity.
