Bayesian Survival Analysis
Bayesian methods have become popular for data analysis and decision making. Modern software has made this possible and methods are now applied in a wide range of scientific application areas, including Bayesian analysis of survival data.
This course is aimed at those who are new to Bayesian statistics and want to develop an understanding and application of Bayesian methods in the context of survival analysis. Emphasis will be on practical data analysis and interpretation; only essential theory will be outlined.
Who Should Attend?
Statisticians and data analysts working in medical and related areas who want an introduction to Bayesian methods for survival analysis. No prior knowledge of Bayesian statistics is required. A working knowledge of survival analysis, specifically the Cox model (including non-proportional hazards and time dependent covariates) and the Weibull model (both proportional hazards and AFT parameterisations) is assumed. Knowledge of SAS is assumed but no prior knowledge of R is assumed.
How You Will Benefit
By the end of the course you will have acquired a firm understanding of Bayesian methods and their flexibility for survival analysis. You will also have acquired a working knowledge of how to conduct Bayesian survival data analysis.
What Do We Cover?
Practical work will be done in: SAS supplemented by R when required
Note: For practical work, participants must bring their own laptop with fully licensed versions of the chosen software.
Related courses: Non-Proportional Hazards: Modelling the Restricted Mean Survival Time using R; Advanced Survival Analysis using R; Introduction to Survival Analysis; Advanced Topics in Survival Analysis and Survival Analysis using R.