TILS210 Survival Models (2–5 cr)
Description
Survival analysis is about datasets where the response variable is the time of an event, for instance the time of diagnosis, time of death, failure time (of some device), or time of purchase. Survival datasets often have censoring because some events happens only after the end of the study period.
In the course, we discuss, for instance, the following topics:
- Terminology of survival analysis, such as survival function and hazard rate.
- Kaplan-Meier survival curve.
- Parametric survival models and their likelihood inference
- Cox proportional hazards model
Learning outcomes
Student who completes the course successfully:
- recognises common features of survival data and knows basic concepts about survival analysis,
- can draw and interpret the Kaplan-Meier survival curve,
- can fit and interpret parametric survival models,
- can fit a Cox proportional hazards model to survival data and interpret the model.
Additional information
Degree students in statistics and data science take the 5 credit course. Degree students from other fields can take either 2 credit or 5 credit course.
The course will be lectured (at least) every 1.5 years.
Description of prerequisites
,5 credits: Statistical inference 1 and 2, Generalized linear models 1 and 2.
2 credits: Data visualization and analysis and From data to model or equivalent background; familiarity with basic usage of R.