Introduction to Survival Analysis
Lecturer: Jan Beyersmann
Exercises taught by: M.Sc. Tobias Bluhmki
|Lectures||Monday, 2:00 p.m.- 4:00 p.m., |
O28 - H21
|Exercise||Wednesday, 2:00 p.m. - 4:00 p.m. N24 - H14|
|Written Exam:||February 8th, 2015, 2:00 p.m. (s.t.!!) - 4:00 p.m. (H21)|
April 9th, 2015, 12:00 p.m. (s.t.) - 2:00 p.m. (H12)
allowed: one book, lecture notes, excercise sheets, etc.
For Students of 'Wirtschaftsmathematik': Course is part of the SOF-Block!
The level of the course is that of a last year's bachelor course in Mathematical Biometry. Some basic programming knowledge in R would be helpful.
|Exam:||Students do not need any requirements In order to be admitted to the exam, but it is highly recommended to work on the excercise sheets!|
Time-to-event data are ubiquitous in fields such as medicine, biology, demography, sociology, economics and reliability theory. In biomedical research, the analysis of time-to-death (hence the name survival analysis) or time to some composite endpoint such as progression-free survival is the most prominent advanced statistical technique. One distinguishing feature is that the data are typically incompletely observed - one has to wait for an event to happen. If the event has not happened by the end of the observation period, the observation is said to be right-censored. This is one reason why the analysis of time-to-event data is based on hazards. This course will emphasize the modern process point of view towards survival data without diving too far into the technicalities.
Lecture Notes (access data will be provided during the first lecture):
P. Dalgaard: Introductory Statistics with R, 2nd Edition, Springer 2008
You are encouraged to return your solutions in pairs until prior to the beginning of the excercise. Answers can also be given in German!
O.O. Aalen, O. Borgan, H. Gjessing: Survival and Event History Analysis - A Process Point of View, Springer 2008
Beyersmann, Allignol, Schumacher: Competing Risks and Multistate Models with R, Springer 2012
Andersen, Borgan, Gill, Keiding: Statistical Models Based on Counting Processes, Springer 1993