| 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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Concepts of Probability and Statistics (1798)
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5,0 stptn |
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Introduction of Bayesian Inference (3562)
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4,0 stptn |
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Medical and Molecular Biology (3564)
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6,0 stptn |
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Following programme components must have been included in your study programme up till now.
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Clinical trials (0385)
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5,0 stptn |
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Longitudinal Data Analysis (3765)
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6,0 stptn |
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Principles of Statistical Inference (3768)
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3,0 stptn |
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Survival Data Analysis (0383)
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3,0 stptn |
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 2nd year Master Biostatistics | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
| 2nd year Master Biostatistics - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | - 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. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can identify relevant stakeholders and their interests, particularly within the programme specialization. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. | | | - DC
| The student can, when building an argumentation, consider different perspectives and interests. | | | - DC
| The student can explain the consequences of his/her work for relevant stakeholders. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.
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| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The course introduces more advanced topics in design and analysis of clinical trials: Bayesian designs, causal inference, adaptive designs, meta-analysis.
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Collective feedback moment ✔
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Distance learning ✔
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Group work ✔
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Homework ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Other evaluation method during teaching period | 30 % |
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| Other | Group work with individual presentations |
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| Transfer of partial marks within the academic year | ✔ |
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| Written exam | 70 % |
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| Multiple-choice questions | ✔ |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | Group work is obligatory |
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| Consequences | No group work = course failure (0) |
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| Additional information | To get the final score, the weighted score is rounded mathematically, unless exam result is less than 50%, in which case the integer part is taken. The maximum final score is 20. To pass the course, the achieved final score has to be at least 10 (i.e., 50%). The homework scores are retained when computing the final score after the second chance exam. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Compulsory course material |
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Photocopies of relevant book chapters and papers (available in an electronic form on BlackBoard). |
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| Recommended reading |
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- Biostatistics in Clinical Trials,Carol K. Redmond; Theodore Colton,Wiley,9780471822110
- Bayesian Approaches to Clinical Trials and Health-Care Evaluation,David J. Spiegelhalter; Keith R. Abrams; Jonathan P. Myles,Wiley,9780471499756,Available as e-book: https://onlinelibrary-wiley-com.bib-proxy.uhasselt.be/doi/book/10.1002/0470092602
- Bayesian Adaptive Methods for Clinical Trials,Scott M. Berry; Bradley P. Carlin; J. Jack Lee; Peter Muller,CRC Press,9781439825488,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=601268&pq-origsite=summon
- The Evaluation of Surrogate Endpoints,Burzykowski, Tomasz; Molenberghs, Geert; Buyse, Marc,1,Springer-Verlag New York,9780387202778,Available as e-book: https://link.springer.com/book/10.1007%2Fb138566
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| Exchange Programme Statistics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly. | - 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. | - EC
| The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | | | - DC
| The student can identify relevant stakeholders and their interests, particularly within the programme specialization. | | | - DC
| The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders. | | | - DC
| The student can, when building an argumentation, consider different perspectives and interests. | | | - DC
| The student can explain the consequences of his/her work for relevant stakeholders. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.
| | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The course introduces more advanced topics in design and analysis of clinical trials: Bayesian designs, causal inference, adaptive designs, meta-analysis.
|
|
|
|
|
|
|
|
|
Collective feedback moment ✔
|
|
|
|
Distance learning ✔
|
|
|
|
|
|
|
|
Group work ✔
|
|
|
|
Homework ✔
|
|
|
|
Period 2 Credits 3,00
| Evaluation method | |
|
| Other evaluation method during teaching period | 30 % |
|
| Other | Group work with individual presentations |
|
|
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
| Written exam | 70 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
| Evaluation conditions (participation and/or pass) | ✔ |
|
| Conditions | Group work is obligatory |
|
|
|
| Consequences | No group work = course failure (0) |
|
|
|
| Additional information | To get the final score, the weighted score is rounded mathematically, unless exam result is less than 50%, in which case the integer part is taken. The maximum final score is 20. To pass the course, the achieved final score has to be at least 10 (i.e., 50%). The homework scores are retained when computing the final score after the second chance exam. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory course material |
| |
Photocopies of relevant book chapters and papers (available in an electronic form on BlackBoard). |
|
|
| Recommended reading |
| |
- Biostatistics in Clinical Trials,Carol K. Redmond; Theodore Colton,Wiley,9780471822110
- Bayesian Approaches to Clinical Trials and Health-Care Evaluation,David J. Spiegelhalter; Keith R. Abrams; Jonathan P. Myles,Wiley,9780471499756,Available as e-book: https://onlinelibrary-wiley-com.bib-proxy.uhasselt.be/doi/book/10.1002/0470092602
- Bayesian Adaptive Methods for Clinical Trials,Scott M. Berry; Bradley P. Carlin; J. Jack Lee; Peter Muller,CRC Press,9781439825488,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/detail.action?docID=601268&pq-origsite=summon
- The Evaluation of Surrogate Endpoints,Burzykowski, Tomasz; Molenberghs, Geert; Buyse, Marc,1,Springer-Verlag New York,9780387202778,Available as e-book: https://link.springer.com/book/10.1007%2Fb138566
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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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