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.
The course will include an introduction to and practical exercises in SAS using the BAYES statements of both PROC PHREG and PROC LIFEREG. We will also make use of the R software for graphics.
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 also assumed. 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 using SAS.
What Do We Cover?
- The Bayesian approach to statistics; prior and posterior distributions
- MCMC methods and diagnostics
- Cox and Weibull models; prior distributions, interpretation and model selection
- Extensions of the Cox model: non-proportional hazards and time dependent covariates
- Questions that classic statistics find difficult or cannot answer
This course has practical exercises written for: R, SAS
Discounts and Consecutive Courses:Advanced Topics in Survival Analysis plus this course: £1285 in total.