De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Longitudinal Data Analysis (3765)
Coordinating lecturer:Prof. dr. Geert MOLENBERGHS 
Co-lecturer:Prof. dr. Geert VERBEKE 

Credits: 6,0
Study load hours: 162
Period: semester 1 (6sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
Mandatory sequentiality bound on the level of programme components
 
 
Group 1
 
  Following programme components must have been included in your study programme in a previous education period
    Generalized Linear Models (5463) 6.0 stptn  
    Project: Multivariate and Hierarchical Data (3565) 8.0 stptn  
 
Or group 2
 
  Following programme components must have been included in your study programme in a previous education period
    Generalized Linear Models (3563) 3.0 stptn  
    Project: Multivariate and Hierarchical Data (3565) 8.0 stptn  
 


Content

This course is dedicated to longitudinal and incomplete data, organized around the central themes:

  • Continuous longitudinal data, with focus on the linear mixed model.
  • Non-Gaussian longitudinal data, with focus on generalized estimating equations and other non-likelihood based methods; with focus on generalized linear mixed models and other likelihood based methods.
  • The relationship between marginal and hierarchical models.
  • Incomplete data.
  • Sensitivity analysis for incomplete data.


Compulsory course material
 

All compulsory course materials (slides, web lectures, assignments, datasets and other materials for assigments) are made available via the electronic learning platform.
 

Recommended course material
 

 Texts on which the course is based:

  • Molenberghs, G. and Verbeke, G. (2005) Models for Discrete Longitudinal Data. New York: Springer.
  • Verbeke, G. and Molenberghs, G. (2000) Linear Mixed Models for Longitudinal Data. New York: Springer.
  • Verbeke, G. and Molenberghs, G. Introduction to Longitudinal Data Analysis. Course Notes. UHasselt & KU Leuven.


Useful additional material:

  • Fitzmaurice, G.M., Davidian, M., Verbeke, G., and Molenberghs, G. (2009) Advances in Longitudinal Data Analysis. London: CRC/Chapman Hall.
  • Molenberghs, G., Fitzmaurice, G.M., Kenward, M.G., Tsiatis, A., and Verbeke, G. (2015) Handbook of Missing Data Methodology. London: CRC/Chapman Hall.
  • Molenberghs, G. and Kenward, M.G. (2007) Missing Data in Clinical Studies. Chichester: John Wiley & Sons.

The course notes, available on BlackBoard, contain a list of primarily books that could usefully be consulted as additional reading, background reading, and in particular also for future reference. 


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Lecture ✔  
Project ✔  
Response lecture ✔  


Evaluation

Semester 1 (6,00sp)

Evaluation method
Written evaluation during teaching period17 %
Paper✔
Oral evaluation during teaching period38 %
Open questions✔
Presentation✔
Written exam8 %
Paper✔
Oral exam37 %
Open questions✔
Presentation✔
Use of study material during evaluation✔
Explanation (English)one's own report; one's own course notes
Evaluation conditions (participation and/or pass)✔
Conditions The student should contribute to and hand in all three reports; the student should present once during the study period and once in the oral exam; the student should respond to questions once in the study period and once during the oral exam.
Consequences Students get an X score if they do not meet all the conditions.

Second examination period

Evaluation second examination opportunity different from first examination opprt
Yes
Explanation (English)Each one of the three written reports that are listed remain valid ie
second chance exam if they were successful. The student has the right
(but not the obligation) to do these reports again. The assignments will
not change in case the student has to do one or more reports again, or
chooses to do one or more reports again. Each report that is done again
will get the score of the more recent report. The reports, taken
together, keep contributing to 25% of the total score, divided as 1/3 of
25% for each of the three reports. The entire score for presentation
is based on the score obtained during the second chance oral exam (50%).
The entire score for open oral questions is based on the score obtained
during the second chance oral exam (25%).


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

     
  •  DC 

    • The student is an effective oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective oral communicator in their own field.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Included in these programmesTolerance3
2nd year Master Bioinformatics N
2nd year Master Bioinformatics - icp N
2nd year Master Biostatistics N
2nd year Master Biostatistics - icp N
2nd year Master Data Science N
2nd year Master Quantitative Epidemiology N
2nd year Master Quantitative Epidemiology - icp N
Exchange Programme Statistics Y



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.