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A new class of survival frailty models based on the GeneralizedInverse-Gaussian (GIG) distributions is proposed. We show that the GIG frailtymodels are flexible and mathematically convenient like the popular gammafrailty model. Furthermore, our proposed class is robust and does not presentsome computational issues experienced by the gamma model. By assuming apiecewise-exponential baseline hazard function, which gives a semiparametricflavour for our frailty class, we propose an EM-algorithm for estimating themodel parameters and provide an explicit expression for the information matrix.Simulated results are addressed to check the finite sample behavior of theEM-estimators and also to study the performance of the GIG models undermisspecification. We apply our methodology to a TARGET (TherapeuticallyApplicable Research to Generate Effective Treatments) data about survival timeof patients with neuroblastoma cancer and show some advantages of the GIGfrailties over existing models in the literature.

Generalized inverse-Gaussian frailty models with application to TARGET neuroblastoma data