Short Course Description

Introduction to Bayesian survival models

Carmen Armero, Universitat de Valencia, Spain
Danilo Alvares, Universitat de Valencia, Spain
Elena Lazaro, Universitat de Valencia, Spain
Survival analysis is one of the most important areas of applied and theoretical research in Statistics, with many
important contributions in life sciences. This small course focuses on Bayesian reasoning in survival models. It con-
tains the most basic elements and procedures in the subject without a strong theoretical approach but a conceptual
and applied perspective that enables comprehensive modeling. Topics will be illustrated by means of real studies
that will be subsequently worked with more depth in practical sessions.
1. Introduction.
2. Basic statistical concepts: survival function, hazard rate, censoring and truncation.
3. Time-to-event regression models: accelerated failure models and proportional hazards (Cox) models.
4. Frailty models.
5. Cure rate models.
6. Joint models of longitudinal and survival data.
R. Christensen, W.O. Johnson, A. J. Branscum and T. E. Hanson (2011). Bayesian Ideas and Data Analysis.
Boca Raton: Chapman and Hall.
J. G. Ibrahim, M.-H. Chen, and D. Sinha (2001). Bayesian Survival Analysis. New York: Springer
J. P. Klein and M. L. Moeschberger (2003). Survival Analysis: Techniques for Censored and Truncated Data
Second Edition. New York: Springer-Verlag