Title: Bayesian survival analysis: comparison of survival probability of hormone receptor status for breast cancer data

Authors: Esin Avc?

Addresses: Department of Statistics, University of Giresun, Giresun, 28000, Turkey

Abstract: Survival analysis is a family of statistical procedures for data analysis for which the outcome variable of interest is time until an event occurs. The Cox model is the most widely used survival model in health sciences, but it is not the only model, parametric models in which the distribution of the event is specified in terms of unknown parameters. Over the last few years, there has been increased interest shown in the application of survival analysis based on Bayesian methodology. In this article, we consider Bayesian survival analysis to compare survival probability of hormone receptor status for breast cancer based on lognormal distribution estimated survival function. The Bayesian approach is implemented using WinBugs.

Keywords: Bayesian survival analysis; survival function; hormone receptor status; breast cancer; survival probability.

DOI: 10.1504/IJDATS.2017.083061

International Journal of Data Analysis Techniques and Strategies, 2017 Vol.9 No.1, pp.63 - 74

Received: 13 Jun 2015
Accepted: 13 Oct 2015
Published online: 20 Mar 2017 *

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