Joint Models

Lecturer: Jan Beyersmann
Exercises taught by:
Karin Schreiber


General Information

LanguageEnglish
Lectures2 h
Exercises1 h

Time and Venue

LecturesTuesday, 10:00 a.m., He22/E18
ExerciseThursday, 05:00 p.m., He22/E18

Exam:

Exam: oral exam (February 9th, 2016)


General Informations:

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, general linear models 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.


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. 


Exercise Sheets:

The exercise sheets are on the SLC.


Literature:

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


 

 

Joint Models

Lecturer: Jan Beyersmann
Exercises taught by:
Karin Schreiber


General Information

LanguageEnglish
Lectures2 h
Exercises1 h

Time and Venue

LecturesTuesday, 10:00 a.m., He22/E18
ExerciseThursday, 05:00 p.m., He22/E18

Exam:

Exam: oral exam (February 9th, 2016)


General Informations:

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, general linear models 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.


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. 


Exercise Sheets:

The exercise sheets are on the SLC.


Literature:

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