# Introduction to Survival Analysis

Lecturer: Jan Beyersmann

Exercises taught by Tobias Bluhmki

General Information

 Language English, unless all students have sufficient knowledge of German Lectures 2 h Exercises 1 h

 Lectures Monday 2:00 p.m. - 4:00 p.m. (H21) Exercise Wednesday 2:00 p.m. -3:00 p.m. (H14)

Exam (open)

 Main Exam: February 08th, 2016 (2:00 p.m. s.t. - 4:00 p.m. H21)Retake exam: March 21st, 2016 (10:00 a.m. s.t. - 12:00 p.m. HeHo 18/E20)

General Informations:

For Students of 'Wirtschaftsmathematik': Course is part of the SOF-Block!

 Prerequisites: Elementary Probability Calculus, Stochastik I and Measure Theory. The level of the course is that of a last year's bachelor course in Mathematical Biometry, but students of Mathematics or Mathematics and Management are welcome, too. Some basic programming knowledge in R would be helpful. Exam: In order to be admitted to the exam, students must have made a meaningful attempt to solve at least 80% of all Problems.

Contents:

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. Statistical methodology for hazards differs from more standard applied statistics. This course will emphasize the modern process point of view towards survival data without diving too far into the technicalities. The level of the course corresponds to one of the many applied introductory texts to survival analysis. After this course, students should be able to understand and use survival analysis as, e.g., required in standard clinical trials with a survival outcome.

Exercise Sheets

on Moodle!

Password is provided during the first lecture/exercise.

Literature:

Klein, M. Moeschberger: Survival Analysis, Springer 2003

O.O. Aalen, O. Borgan, H. Gjessing: Survival and Event History Analysis - A Process Point of View, Springer 2008

J. Beyersmann, A. Allignol, M. Schumacher: Competing Risks and Multistate Models with R, Springer 2012

# Note

Last exercise (Solution Exam) is switched to N24, Room 104.