Survival and Event History Analysis

Lecturer Jan Beyersmann
Exercises taught by
Karin Schiefele


General Informations

Language English

Lectures    4h
Exercises  2h

The Exercise Sheets are on the SLC.

Prerequisites: 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.

Exam: In order to be admitted to the exam, students must have made a meaningful attempt to solve at least 80% of all Problems.


Time and Venue 

Lectures   Tuesday, 14:00 - 16:00, O28-2001
                Wednesday, 12:00 - 14:00, He22 E18

Exercises Wednesday, 8:30 - 10:00, N24-131


Exam 

23.07.2014, 12:00, H1
09.10.2014, 15:00, H14


Contents

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.

 


Literature

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

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

Notice

Skript
Das Vorlesungsskript ist ab jetzt im SLC verfuegbar.

Survival and Event History Analysis

Lecturer Jan Beyersmann
Exercises taught by
Karin Schiefele


General Informations

Language English

Lectures    4h
Exercises  2h

The Exercise Sheets are on the SLC.

Prerequisites: 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.

Exam: In order to be admitted to the exam, students must have made a meaningful attempt to solve at least 80% of all Problems.


Time and Venue 

Lectures   Tuesday, 14:00 - 16:00, O28-2001
                Wednesday, 12:00 - 14:00, He22 E18

Exercises Wednesday, 8:30 - 10:00, N24-131


Exam 

23.07.2014, 12:00, H1
09.10.2014, 15:00, H14


Contents

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.

 


Literature

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

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

Notice

Skript
Das Vorlesungsskript ist ab jetzt im SLC verfuegbar.