Lecturer: Jan Beyersmann
Exercises taught by Tobias Bluhmki
Time and Venue
|Tuesday 10:00 a.m. - 12:00 p.m. (HeHo 18/220)|
|Exercise||Thursday 17:00 p.m. - 18:00 p.m. (HeHo 22/E18)|
In order to be admitted to the exam, students must have made a meaningful attempt to solve at least 80% of all Problems.
Prerequisites: Stochastik I. The level of the course is roughly that of a first year's master course in Mathematical Biometry, but students of Mathematics or Mathematics and Management are welcome, too. Basic knowledge of standard survival analysis, general linear models and of R is helpful.
Contents: Roughly speaking, survival analysis focuses on the occurrence of events, while the analysis of longitudinal data focuses on the development of covariates or markers over the course of time. A classical example from medical research is time-to-death in HIV patients and CD4 cell counts; the latter measure viral load. However, the distinction between the analysis of survival data and the analysis of longitudinal measurements is artificial. Joint models aim at an integrated analysis. As these models may not have found their final form yet, the lecture potentially touches upon rather recent research work towards the end of the semester, including causal modeling and updated prediction.
The exercise sheets are moodle: Please participate in Joint Models --> Link
D Rizopoulos: Joint models for longitudinal and time-to-event data (with applications in R). CRC, Boca Raton, 2012
O Aalen: Armitage lecture 2010: Understanding treatment effects: the value of integrating longitudinal data and survival analysis, Statistics in Medicine 2012, 31, 1903--1917
First lecture: Tuesday, October 13th
First exercise: Thursday, October 22nd