| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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For following programme components you must have acquired a credit certificate, exemption, already tolerated unsatisfactory grade or selected tolerable unsatisfactory grade.
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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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| 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
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| Written evaluaton during teaching periode | 17 % |
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| Oral evaluation during teaching period | 38 % |
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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 a 0 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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| Prerequisites |
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Students should have a working knowledge of Analysis of Variance and Regression methods, as well as Generalized Linear Models.
Students taking the two-year program should have succeeded in Project-Multivariate and Hierarchical Data. |
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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) {\itshape Handbook of Misisng Data Methodology. London: CRC/Chapman Hall.
- Molenberghs, G. and Kenward, M.G. (2007) Missing Data in Clinical Studies. Chichester: John Wiley & Sons.
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1 examination regulations art.1.3, section 4. |
| 2 examination regulations art.4.7, section 2. |
3 examination regulations art.2.2, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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