Bayesian Analysis of Masked Competing Risks Data Based on Proportional Subdistribution Hazards Model
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.
Other Information
Published in: Mathematics
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://doi.org/10.3390/math10173045
Funding
Open Access funding provided by the Qatar National Library
History
Language
- English
Publisher
MDPIPublication Year
- 2022
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International LicenseInstitution affiliated with
- Qatar University
- College of Arts and Sciences - QU