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A competing risks cure frailty model: An application to relapse-free survival of breast cancer patients
Author(s):
1. Vahid Ghavami: Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
2. Mahmood Mahmoudi: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
3. Abbas Rahimi Foroushani: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
4. Hossein Baghishani: Department of Applied Mathematics, Shahrood University of Technology, Shahrood, Iran
5. Mehdi Yaseri: Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
6. Fatemeh Homaei Shandiz: Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
Abstract:
If there is unobserved heterogeneity among susceptible patients in competing risks cure models, applying the methods that do not consider this heterogeneity may lead to invalid results. Therefore, this study aimed to introduce a model to cover the above properties of survival studies. We introduced a unified model by combining a parametric mixture cure gamma frailty model and vertical modeling of competing risks. We obtained estimates of parameters by an iterative method and Laplace transform technique. Then, we calculated the cumulative incidence functions (CIFs) and related confidence bounds using a bootstrap approach. We conducted an extensive simulation study to evaluate the performance of the proposed model. The results of the simulation study showed the superior performance of our proposed competing risks cure frailty model. Finally, we applied the proposed method to analyze a real dataset of breast cancer patients.
Page(s): 591-605
Published: Journal: Pakistan Journal of Statistics and Operation Research, Volume: 17, Issue: 3, Year: 2021
Keywords:
Frailty model , Cumulative incidences , Survival analysis , Competing risks , Mixture cure model
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