A Bayesian Approach to Competing Risks Model with Masked Causes of Failure and Incomplete Failure Times
We present a Bayesian approach for analysis of competing risks survival data with masked causes of failure. This approach is often used to assess the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time for the remaining subjects. Such data, known as partly interval-censored data, usually result from periodic inspection in production engineering. In this study, Dirichlet and Gamma processes are assumed as priors for masking probabilities and baseline hazards. Markov chain Monte Carlo (MCMC) technique is employed for the implementation of the Bayesian approach. The effectiveness of the proposed approach is illustrated with simulated and production engineering applications.
Other information
Published in: Mathematical Problems in Engineering
License: http://creativecommons.org/licenses/by/4.0
See article on publisher's website: http://dx.doi.org/10.1155/2020/8248640
Funding
Open Access funding provided by the Qatar National Library.
History
Language
- English
Publisher
HindawiPublication Year
- 2020
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International License.Institution affiliated with
- Qatar University
- College of Arts and Sciences - QU