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

Authors :
Yousif, Yosra
Elfaki, Faiz
Hrairi, Meftah
Adegboye, Oyelola
Source :
Mathematics (2227-7390); Sep2022, Vol. 10 Issue 17, p3045, 10p
Publication Year :
2022

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22277390
Volume :
10
Issue :
17
Database :
Complementary Index
Journal :
Mathematics (2227-7390)
Publication Type :
Academic Journal
Accession number :
159035573
Full Text :
https://doi.org/10.3390/math10173045