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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Group 1 |
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Following programme components must have been included in your study programme in a previous education period
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Generalized Linear Models DL (3580)
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6.0 stptn |
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Project: Multivariate and Hierarchical Data DL (3582)
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8.0 stptn |
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Or group 2 |
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Following programme components must have been included in your study programme in a previous education period
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Generalized Linear Models DL (3580)
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3.0 stptn |
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Project: Multivariate and Hierarchical Data DL (3582)
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8.0 stptn |
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There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Bioinformatics - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical |  |
| second year Master Biostatistics - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| second year Data Science - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| second year Quantitative Epidemiology - distance learning | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
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| | | Learning outcomes |
- EC
| The student is capable of acquiring new knowledge. | - 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 writer, 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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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.
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Distance learning ✔
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Project ✔
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Response lecture ✔
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 17 % |
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| Oral evaluation during teaching period | 38 % |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | One's own report ; one's own course notes. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | All components have to be taken up. |
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| Consequences | Students get an "X" score if they do not meet the condition. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| 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. |
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| Compulsory course material |
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All compulsory cousre material (slides, web lectures, assignments, datasets and other materials for assigments) are made available via the electronic learning platform. |
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| Recommended course material |
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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. |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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