Survival and Event History Analysis

Lecturer Jan Beyersmann
Exercises taught by
Jan Feifel

General Informations

Language English

Lectures    4h
Exercises  2h

Prerequisites: Elementary Probability Calculus, Stochastic I, Measure and Integration Theory, basic Programming Skills.

The level of the course is roughly that of a first year's master course in Mathematical Biometry. Basic knowledge of standard survival analysis and of R is helpful, but not mandatory.

Time and Venue 

Lectures   Monday 10h - 12h & Friday 10h - 12h; Helmholtzstraße 18, Room 120

Exercises Wednesday 12h - 14h, Helmholtzstraße 22, Room 

The lecture starts Friday, April 26, 2019 (Easter Monday is on the 22th April). 

Oral Exam TBA

Exercise Sheets

Will be available on Moodle. Password is provided during the first lecture!


Time-to-event data are omnipresent 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. At the heart of the statistical methodology are counting processes, martingales and stochastic integrals. This methodology allows for the analysis of time-to-event data which are more complex than composite endpoints and will be the topic of this course. The relevance of these methods is, e.g, illustrated in the current debate on how to analyse adverse events. Time permitting, we will also discuss connections between causal modelling and event histories.


Aalen, Borgan, Gjessing: Survival and Event History Analysis, Springer 2008

Andersen, Borgan, Gill, Keiding: Statistical Models Based on Counting Processes, Springer 1993

Beyersmann, Allignol, Schumacher: Competing Risks and Multistate Models with R, Springer 2012