Models for Longitudinal and Incomplete Data, Short Course

  • N/A
    Application Deadline
  • 6 days
  • Tuition
    Tuition (Year)
    Tuition (Year)
  • English (take IELTS)
University rank #40 ,
We first present linear mixed models for continuous hierarchical data. The focus lies on the modeler’s perspective and on applications. Emphasis will be on model formulation, parameter estimation, and hypothesis testing, as well as on the distinction between the random-effects (hierarchical) model and the implied marginal model.
  • Overview
  • Programme outline
  • Key facts
  • Admission requirements
  • Fees and funding


We first present linear mixed models for continuous hierarchical data. The focus lies on the modelers perspective and on applications. Emphasis will be on model formulation, parameter estimation, and hypothesis testing, as well as on the distinction between the random-effects (hierarchical) model and the implied marginal model. Apart from classical model building strategies, many of which have been implemented in standard statistical software, a number of flexible extensions and additional tools for model diagnosis will be indicated.

Second, models for non-Gaussian data will be discussed, with a strong emphasis on generalized estimating equations (GEE) and the generalized linear mixed model (GLMM). To usefully introduce this theme, a brief review of the classical generalized linear modeling framework will be presented.

Similarities and differences with the continuous case will be discussed. The differences between marginal models, such as GEE, and random-effects models, such as the GLMM, will be explained in detail.
Third, it is oftentimes necessary to consider fully non-linear models for longitudinal data. We will discuss such situations, and place some emphasis on the non-linear mixed-effects model.

Fourth, non-linear mixed models will be discussed. Applications in the PK/PD world will be brought to the front. Fifth, when analyzing hierarchical and longitudinal data, one is often confronted with missing observations, i.e., scheduled measurements have not been made, due to a variety of (known or unknown) reasons. It will be shown that, if no appropriate measures are taken, missing data can cause seriously jeopardize results, and interpretation difficulties are bound to occur.

Methods to properly analyze incomplete data, under flexible assumptions, are presented. Key concepts of sensitivity analysis are introduced.

All developments will be illustrated with worked examples using the SAS System. However, the course is conceived such that it will be of benefit to both SAS users and users of other platforms.

One day of this 6-day training is dedicated to hands-on computation. This includes, not only classroom exercises, but also the option to analyse participants owndata! For the latter aspects, the SAS System will be used.

Detailed Programme Facts

  • Full-time duration 6 days
  • Study intensity Full-time
  • Languages
    • English
  • Delivery mode
    On Campus
  • Entry Level
  • Skill Disciplines
    Research Techniques

Programme Structure

Course Materials
  • A .pdf file with the course material will be made available.
  • Background reading:
    Verbeke, G. and Molenberghs, G. (2000) Linear Mixed Models for Longitudinal Data. New York: Springer.
    Molenberghs, G. and Kenward, M.G. (2007) Missing Data in Clinical Studies. Chichester: John Wiley & Sons.
    Molenberghs, G. and Verbeke, G. (2005) Models for Repeated Discrete Data. New York: Springer.


The targeted audience includes methodological and applied statisticians and researchers in industry, public health organizations, contract research organizations, and academia.Important: The course will also serve for the Master in Statistics.


Geert Verbeke is Professor in Biostatistics at Katholieke Universiteit Leuven and Uni versiteit Hasselt in Belgium. He received the B.S. degree in mathematics (1989) from the Katholieke Universiteit Leuven, the M.S. in biostatistics (1992) from Universiteit Hasselt, and earned a Ph.D. in biostatistics (1995) from the Katholieke Universiteit Leuven. Geert Ver beke has published extensively on longitudinal data analyses. He has held visiting positions at the Gerontology Research Center and the Johns Hopkins University (Baltimore, MD). Geert Verbeke is Past President of the Belgian Region of the International Biometric Society, International Program Chair for the International Biometric Conference in Montreal (2006), Board Member of the American Statistical Association. He is past Joint Editor of the Journal of
the Royal Statistical Society, Series A (20052008) and currently editor of Biometrics (2010 2012). He is the director of the Leuven Center for Biostatistics and statistical Bioinformatics (L-BioStat), and vice-director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), a joint initiative of the Hasselt and Leuven universities in Belgium.

Geert Molenberghs is Professor of Biostatistics at the Universiteit Hasselt and Katholieke Universiteit Leuven in Belgium. He received the B.S. degree in mathematics (1988) and a Ph.D. in biostatistics (1993) from the Universiteit Antwerpen. Dr Molenberghs published methodological work on surrogate markers in clinical trials, categorical data, longitudinal data analysis, and on the analysis of non-response in clinical and epidemiological studies. He served as Joint Editor for Applied Statistics (2001-2004), Co-editor for Biometrics (20072009) and as President of the International Biometric Society (2004-2005). He currently is Co-editor for Biostatistics (20102012). He was elected Fellow of the American Statistical Association and received the Guy Medal in Bronze from the Royal Statistical Society. He has held visiting positions at the Harvard School of Public Health (Boston, MA). He is founding director of the Center for Statistics at Hasselt University and currently the director of the Interuniversity Institute for Biostatistics and statistical Bioinformatics, I-BioStat, a joint initiative of the
Hasselt and Leuven universities.
Geert Molenberghs and Geert Verbeke are editors and authors of several books on the use of linear mixed models for the analysis of longitudinal data (Springer Lecture Notes 1997, Springer Series in Statistics 2000, Springer Series in Statistics 2005, Chapman Hall/CRC 2007), and they have taught numerous short and longer courses on the topic in universities as well as industry, in Europe, North America, Latin America, and Australia. Both instructors received several Excellence in Continuing Education Awards of the American Statistical Association, for courses at Joint Statistical Meetings.

English Language Requirements

This programme requires students to demonstrate proficiency in English.

Take IELTS test

Academic Requirements

Throughout the course, it will be assumed that the participants are familiar with basic statistical modeling concepts, including linear models (regression and analysis of variance), as well as generalized linear models (logistic and Poisson regression) and basic knowledge of mixed and multilevel models. Moreover, pre-requisite knowledge should also include general estimation and testing theory (maximum likelihood, likelihood ratio). When registering for this course, you have to mention the topics you have followed before and/or indicate where you became acquainted with the requested material.

Tuition Fee Per Year

  • EUR 400 International
  • EUR 400 EU/EEA
  • Staff and students Association KU Leuven and PhD students, non-KU Leuven € 400
  • Non profit/social sector € 625
  • Private sector € 1500


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