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

Yousif, Yosra, Elfaki, Faiz, Hrairi, Meftah, and Adegboye, Oyelola (2022) Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model. Mathematics, 10 (17). 3045.

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DOI: 10.3390/math10173045

View at Publisher Website: https://doi.org/10.3390/math10173045

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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 ID:	76073
Item Type:	Article (Research - C1)
ISSN:	2227-7390
Keywords:	competing risks; masked causes of failure; subdistribution hazards; MCMC; Bayesian analysis
Copyright Information:	© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited:	19 Sep 2022 01:42
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics @ 100%
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