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-use Cox regression model.
Examples used will be drawn from a variety of applications in medicine and health.
Practical work will be based around the statistical software R; see https://www.r-project.org/.
All training is online and will be delivered live each day between 09:00 and 17:30 (GMT+1). Delivery platform: Zoom, which may be freely accessed. Questions may be asked using Zoom's chat box. Note our online courses are delivered by a team of two presenters, meaning at least one presenter is always available to provide additional support. During presentations the team member who is not speaking can take questions in addition to the presenter.
Who Should Attend?
Medical and health professionals who need analytical tools for making inferences from survival data. Participants will be assumed to have a working knowledge of:
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?
The course does not cover parametric modelling of survival data, such as the Weibull proportional hazards regression model.
Practical work will be done in R.
Note: For practical work, participants must download and install a number of CRAN packages in R. This must be done prior to the start of the course.