Lecturer Jan Beyersmann

Exercises Judith Vilsmeier

General Informations

Language English

Lectures   4h
Exercises  2h

Prerequisites: Elementary Probability Calculus and Statistics, 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 or Mathematics or Mathematical Data Sience or Wirtschaftsmathematik. Basic knowledge of R is helpful.

Time and Venue (TBC)

Lectures Tuesday 10h - 12h & Thursday 14h - 16h; Helmholtzstraße 18, Room 220

Exercises  Wednesday 16h - 18h, Helmholtzstraße 18, Room 120

Exam oral (TBC)

Exercise Sheets

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

Contents

Time-to-event data are omnipresent in fields such as medicine, biology, demography, sociology, economics, reliability theory and data sience. 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 has, e.g, recently been illustrated in
Covid-19 trials. Time permitting, we will also discuss connections between causal modelling and event histories. A quick check of the scientific program of this year´s German biostatistical conference also illustrates the prominence of the field.

Literature

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

Semesterapparat