Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model

Yosra Yousif, Faiz Elfaki (), Meftah Hrairi and Oyelola Adegboye
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Yosra Yousif: Department of Mechanical Engineering, Faculty of Engineering, International Islamic University Malaysia (IIUM), P.O. Box 10, Kuala Lumpur 50728, Malaysia
Faiz Elfaki: Statistics Program, Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha P.O. Box 2713, Qatar
Meftah Hrairi: Department of Mechanical Engineering, Faculty of Engineering, International Islamic University Malaysia (IIUM), P.O. Box 10, Kuala Lumpur 50728, Malaysia
Oyelola Adegboye: Public Health & Tropical Medicine, College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, QLD 4811, Australia

Mathematics, 2022, vol. 10, issue 17, 1-10

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.

Keywords: competing risks; masked causes of failure; subdistribution hazards; MCMC; Bayesian analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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