Generalized inverse-Gaussian frailty models with application to TARGET neuroblastoma data
KAUST DepartmentStatistics Program, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
Embargo End Date2021-11-30
Permanent link to this recordhttp://hdl.handle.net/10754/662520
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AbstractA 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. A piecewise-exponential baseline hazard function is employed, yielding flexibility for the proposed class. Although a closed-form observed log-likelihood function is available, simulation studies show that employing an EM-algorithm is advantageous concerning the direct maximization of this function. Further simulated results address the comparison of different methods for obtaining standard errors of the estimates and confidence intervals for the parameters. Additionally, the finite-sample behavior of the EM-estimators is investigated and the performance of the GIG models under misspecification assessed. We apply our methodology to a TARGET (Therapeutically Applicable Research to Generate Effective Treatments) data about the survival time of patients with neuroblastoma cancer and show some advantages of the GIG frailties over existing models in the literature.
CitationPiancastelli, L. S. C., Barreto-Souza, W., & Mayrink, V. D. (2020). Generalized inverse-Gaussian frailty models with application to TARGET neuroblastoma data. Annals of the Institute of Statistical Mathematics. doi:10.1007/s10463-020-00774-z
SponsorsWe thank the Associate Editor and two anonymous Referees for their insightful comments and suggestions that lead to a great improvement of the paper. We also thank the National Cancer Institute (Office of Cancer Genomics) for granting us permission to use the TARGET Neuroblastoma Clinical data for publication. W. Barreto-Souza would also like to acknowledge support for his research from the KAUST Research Fund, NIH 1R01EB028753-01, and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq-Brazil, Grant number 305543/2018-0). Part of this work is from the Master’s Thesis of Luiza S.C. Piancastelli realized at the Department of Statistics of the Universidade Federal de Minas Gerais, Brazil.