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

Esin Avc?

International Journal of Data Analysis Techniques and Strategies, 2017, vol. 9, issue 1, 63-74

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. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=83061 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:9:y:2017:i:1:p:63-74

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().