Lecturer Jan Beyersmann

Exercises Alexander Stemke

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

Exercises  Wednesday 12h - 14h, Helmholtzstraße 18, Room 120

The lecture starts Wednesday, April 20, 2022 (Easter Monday is on the 18th April). 

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 is, e.g, illustrated in the current 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 joint statistical meeting 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