Survival Analysis for Medical and Health Professionals
Survival data arise in many medical areas. Examples include time to death after an operation, time to recovery from an accident, and duration of pain relief.
One particular aspect of time-to-event data is censoring, where the time to an event is not known exactly; it is only known to be greater than a certain value. The methods of analysis for survival data fully encompass the issue of censoring.
The course is a basic practical introduction to some of the commonly-used tools for analysing survival data. Statistical theory underlying the different approaches is kept to a minimum, and emphasis is placed on how to summarise data and how to interpret common hypothesis tests. The course also introduces and explains the concept of modelling survival data based on the widely-used Cox regression model.
For practical work, participants may choose from the statistical packages R, SAS and Stata. Examples used will be drawn from a variety of applications in medicine and health.
Who Should Attend?
Medical and health professionals who need analytical tools for making inferences from survival data. Participants will be assumed to have some knowledge of elementary statistical techniques (as are covered in A Review of Basic Statistics) and regression modelling (see our course Regression Analysis: A Hands-on Approach).
How You Will Benefit
You will acquire practical experience in the use of commonly-used techniques for the analysis of survival data, and an appreciation of more complex methods.
What Do We Cover?
- Survival data: properties, examples, issues of censoring
- Summary statistics and graphics: survival curves and the Kaplan-Meier estimate
- Comparison of two groups: common hypothesis tests
- The concept of a hazard of an event
- The Cox proportional hazards model
- Comparison of Cox regression models
- Predictions from the Cox model
- Stratified Cox regression model
- Time varying covariates.
This course has practical exercises written for: R, SAS, Stata