IJIRST (International Journal for Innovative Research in Science & Technology)ISSN (online) : 2349-6010

 International Journal for Innovative Research in Science & Technology

Proportional Hazards Regression Model for Time to Event Breast Cancer Data: A Bayesian Approach


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International Journal for Innovative Research in Science & Technology
Volume 3 Issue - 8
Year of Publication : 2017
Authors : T Leo ALexander

BibTeX:

@article{IJIRSTV3I8045,
     title={Proportional Hazards Regression Model for Time to Event Breast Cancer Data: A Bayesian Approach},
     author={T Leo ALexander},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={8},
     pages={45--51},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I8045.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}



Abstract:

The paper deals with a Bayesian based Cox regression model to consider strategies for performing survivability of patients with breast cancer through Bayesian aspects. The proportional hazards model (PHM) in the context of survival data analysis, is same as Cox model and was introduced by Cox (1972) in order to estimate the effects of different covariates influencing the times-to-event data. It’s well known that Bayesian analysis has the advantage in dealing with censored data and small sample over frequentist methods. Therefore, in this paper it deliberately explores the PHM for right-censored death times from Bayesian perspective, and compute the Bayesian estimator based on the Markov Chain Monte Carlo (MCMC) method. In particular it focuses on the approaches based on Gibbs sampler. Such approaches may be implemented using the publically available software BUGS. It aims to compare and apply Bayesian models of survivability for prediction of patients with breast cancer using outcome as explanatory variables and to produce better descriptions to survival of patients with breast cancer and of subgroups of patients with different survival characteristics.


Keywords:

Proportional Hazards Model, Posterior Distributions, Markov Chain Monte Carlo, Gibbs Sampler, Winbugs


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