A Bayesian approach to Cox-Gompertz model

Ata Tutkun, N and Demirhan, H 2016, 'A Bayesian approach to Cox-Gompertz model', Hacettepe Journal of Mathematics and Statistics, vol. 45, no. 5, pp. 1621-1640.

Document type: Journal Article
Collection: Journal Articles

Title A Bayesian approach to Cox-Gompertz model
Author(s) Ata Tutkun, N
Demirhan, H
Year 2016
Journal name Hacettepe Journal of Mathematics and Statistics
Volume number 45
Issue number 5
Start page 1621
End page 1640
Total pages 20
Publisher Hacettepe University
Abstract Survival analysis has a wide application area from medicine to market- ing and Cox model takes an important part in survival analysis. When the distribution of survival data is known or it is appropriate to assume a survival distribution, use of a parametric form of Cox model is em- ployed. In this article, we take into account Cox-Gompertz model from the Bayesian perspective. Considering the di culties in parameter es- timation in classical setting, we propose a simple Bayesian approach for Cox-Gompertz model. We derive full conditional posterior distributions of all parameters in Cox-Gompertz model to run Gibbs sampling. Over an extensive simulation study, estimation accuracies of the classical Cox model and classical and Bayesian settings of Cox-Gompertz model are compared with each other by generating exponential, Weibull, and Gompertz distributed survival data sets. Consequently, if survival data follows Gompertz distribution, most accurate parameter estimates are obtained by the Bayesian setting of Cox-Gompertz model. We also provide a real data analysis to illustrate our approach. In the data analysis, we observe the importance of use of the most accurate model over the survival probabilities of censored observations.
Subject Applied Statistics
Statistical Theory
Keyword(s) Gompertz
Cox model
Gibbs sampling
Bayesian analysis
full conditional
parametric model.
DOI - identifier 10.15672/HJMS.20158012506
Copyright notice Copyright © 2016 Faculty of Science of Hacettepe University
ISSN 1303-5010
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