Longitudinal Data Analysis DL (3784)
  
Coordinating lecturer :Prof. dr. Geert MOLENBERGHS 
  
Co-lecturer :Prof. dr. Geert VERBEKE 

Language of instruction : English
Credits: 6,0
  
Period: semester 1 (6sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
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 DL (5465) 6.0 stptn
    Project: Multivariate and Hierarchical Data DL (3582) 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 DL (3580) 3.0 stptn
    Project: Multivariate and Hierarchical Data DL (3582) 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.


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
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 All components have to be taken up.
Consequences Students get an X score if they do not meet the condition.

Second examination period

Evaluation second examination opportunity different from first examination opprt
Yes
Explanation (English)Each one of the three projects that resulted in a pass score, can be
maintained during the second chance exam. The students can but do not
have to do such reports again. Every report that resulted in a fail has
to be done again. The assignments for these reports remain the same
as for the first chance exam. The structure of the second chance exam
remains the same as for the first chance exam.
 

Compulsory course material
 

All compulsory cousre material (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. 


Learning outcomes
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 oral communicator 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 in their own field.

     
  •  DC 

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

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
second year Data Science - distance learning N
second year Master Bioinformatics - distance learning N
second year Master Biostatistics - distance learning N
second year Quantitative Epidemiology - distance learning N



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.