Bayesian analysis of masked competing risks data based on proportional subdistribution hazards model

Yousif, Yosra and Elfaki, Faiz Ahmed Mohamed and Hrairi, Meftah and Adegboye, Oyelola (2022) Bayesian analysis of masked competing risks data based on proportional subdistribution hazards model. Mathematics, 10 (17). pp. 1-10. ISSN 2227-7390

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Abstract

Masked issues can emerge when dealing with competing risk data. Such issues are exemplified by the cause of a particular failure not being directly exhibited for all units to observe but only proven to be a subset of possible causes of failure. For assessing the impact of explanatory variables (covariates) on the cumulative incidence function (CIF), a process of Bayesian analysis is discussed in this paper. The symmetry assumption is not imposed on the masking probabilities and independent Dirichlet priors assigned to them. The Markov Chain Monte Carlo (MCMC) technique is utilized to implement the Bayesian analysis. The effectiveness of the developed model is tested via numerical studies, including simulated and real data sets.

Item Type:	Article (Journal)
Uncontrolled Keywords:	competing risks; masked causes of failure; subdistribution hazards; MCMC; Bayesian analysis
Subjects:	Q Science > QA Mathematics > QA276 Mathematical Statistics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Prof. Dr. Meftah Hrairi
Date Deposited:	28 Aug 2022 15:46
Last Modified:	28 Aug 2022 15:46
URI:	http://irep.iium.edu.my/id/eprint/99656

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