Survival and Event History Analysis
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 & Tuesday 14h - 16h; Helmholtzstraße 18, Room 120
Exercises Wednesday 12h - 14h, Helmholtzstraße 22, Room E04
The lecture starts Tuesday, April 18, 2017 (Easter Monday is on the 17th April). On Wednesday, April 19, 2017, 12h - 14h, the lecture (but yet no exercises ) will take place in Helmholtzstraße 22, Room E04.
Oral Exam Tuesday, July 18, 2017 (to be confirmed)
In order to be admitted to the exam, students must have made a meaningful attempt to solve at least 80% of all problems.
Will be available on moodle. Password is provided during the first lecture!
You may turn in solutions in pairs (two students, one solution; but not more than two).
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