A Bayesian Survival Analysis of a Historical Dataset: How Long Do Popes Live?

ABSTRACT

University courses in statistical modeling often place great emphasis on methodological theory, illustrating it only briefly by means of limited and repeatedly used standard examples. Unfortunately, this approach often fails to actively engage and motivate students in their learning process. The teaching of statistical topics such as Bayesian survival analysis can be enhanced by focusing on innovative applications. Here, we discuss the visualization and modeling of a dataset of historical events comprising the post-election survival times of popes. Inference, prediction, and model checking are performed in the Bayesian framework, with comparisons being made with the frequentist approach. Further opportunities for similar statistical investigations are outlined. Supplementary materials for this article are available online.

KEYWORDS:

• BUGS
• Censoring
• Frequentist survival analysis
• JAGS
• Lexis diagram
• Model checking
• Pope Francis
• Posterior predictive distribution
• Weibull residuals

Supplementary Materials

The online supplementary materials contain the appendices for the article.

Acknowledgments

We would like to thank the Editor, Dr Nicole Lazar, the Associate Editor, the Reviewers, and the editorial team for very constructive suggestions that have improved the content and presentation of this contribution.

Additional information

Funding

This research was supported by the National Institute for Health Research (NIHR) Great Ormond Street Hospital Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the National Health Service (NHS), the NIHR or the UK Department of Health.