  A new class of survival frailty models based on the Generalized
Inverse-Gaussian (GIG) distributions is proposed. We show that the GIG frailty
models are flexible and mathematically convenient like the popular gamma
frailty model. Furthermore, our proposed class is robust and does not present
some computational issues experienced by the gamma model. By assuming a
piecewise-exponential baseline hazard function, which gives a semiparametric
flavour for our frailty class, we propose an EM-algorithm for estimating the
model parameters and provide an explicit expression for the information matrix.
Simulated results are addressed to check the finite sample behavior of the
EM-estimators and also to study the performance of the GIG models under
misspecification. We apply our methodology to a TARGET (Therapeutically
Applicable Research to Generate Effective Treatments) data about survival time
of patients with neuroblastoma cancer and show some advantages of the GIG
frailties over existing models in the literature.
