Bayesian Survival Analysis of Head and Neck Cancer Data Using Lognormal Model

Abstract

The paper considers a lognormal model for the survival times and obtains a Bayes solution by means of Gibbs sampler algorithm when the priors for the parameters are vague. The formulation given in the paper is mainly focused for censored data problems though it is equally well applicable for complete data scenarios as well. For the purpose of numerical illustration, we considered two real data sets on head and neck cancer patients when they have been treated using either radiotherapy or chemotherapy followed by radiotherapy. The paper not only compares the survival functions for the two therapies assuming a lognormal model but also provides a model compatibility study based on predictive simulation results so that the choice of lognormal model can be justified for the two data sets. The ease of our analysis as compared to an earlier approach is certainly an advantage.

Keywords:

• Gibbs sampler
• Hazard rate
• Head and neck cancer
• Lognormal model
• Markov chain
• Monte Carlo
• Survival function

Acknowledgment

The authors wish to express their thankfulness to the Associate Editor and anonymous referees of the Journal for improving the earlier version of the manuscript.