| 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 (3563)
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3,0 stptn |
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Project: Multivariate and Hierarchical Data (3565)
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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 | |
 | 2nd year Master Bioinformatics | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical |  |
| 2nd year Master Bioinformatics - icp | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 2nd year Master Biostatistics | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 2nd year Master Biostatistics - icp | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 2nd year Master Data Science | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 2nd year Master Quantitative Epidemiology | Compulsory | 162 | 6,0 | 162 | 6,0 | Yes | No | Numerical | |
| 2nd year Master Quantitative Epidemiology - icp | 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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Lecture ✔
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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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| 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 | 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. |
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| Consequences | Failure to do one or more components listed above results in 0 score. |
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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 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%). |
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| Prerequisites |
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Generalized linear models (3563) AND Project: Multivariate and Hierarchical Data (3565) |
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| Compulsory course material |
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All compulsory course materials (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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| Exchange Programme Statistics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
|
| | | 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. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
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.
|
|
|
|
|
|
|
|
|
Distance learning ✔
|
|
|
|
Lecture ✔
|
|
|
|
Project ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Period 1 Credits 6,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 17 % |
|
|
|
|
| Oral evaluation during teaching period | 38 % |
|
|
|
|
|
|
|
| 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 | Failure to do one or more components listed above results in 0 score. |
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| 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%). |
|
|
|
|
|
| Prerequisites |
| |
Generalized linear models (3563) AND Project: Multivariate and Hierarchical Data (3565) |
|
|
| 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. |
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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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