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

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  • Esin Avc?

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.

Suggested Citation

  • Esin Avc?, 2017. "Bayesian survival analysis: comparison of survival probability of hormone receptor status for breast cancer data," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 9(1), pages 63-74.
    • Handle: RePEc:ids:injdan:v:9:y:2017:i:1:p:63-74
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