| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Quantitative Epidemiology | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical |  |
| 1st year Master Quantitative Epidemiology - icp | Compulsory | 108 | 4,0 | 108 | 4,0 | 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. | | | - DC
| ... correctly using state-of-the-art design methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the societal relevance of statistics 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 knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | - 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 methodologically, thus contributing to scientific research 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 application. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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This course introduces the student to the basics of epidemiology. The focus is on the methodology of the study of the occurrence of health and disease in human (and by extent in animal) populations. The course starts with an overview of the history and present of epidemiology. Then, it introduces the measures of occurrence and association, and the concepts of causality and systematic error. Last, the main study designs used in epidemiology are discussed. In particular, the general concepts in relation to cohort, case-control and trials are explained.
By the end of this course, students should be able to:
- understand basic terminology and basic principles of epidemiology,
- calculate and interpret the different measures of occurrence, association and impact used in epidemiological research
- understand causality concepts and identify threats to causality in epidemiological studies (bias)
- accurately present different types of epidemiological studies.
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Lecture ✔
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Self-study assignment ✔
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Period 2 Credits 4,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 40 % |
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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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Hand-outs, slides, selected readings |
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| Recommended reading |
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- Epidemiology. An introduction,Kenneth J. Rothman,2,Oxford University Press,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=5763681&pq-origsite=summon
- Epidemiology by Design,Daniel Westreich,1,Oxford University Press
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| 1st year Master Bioinformatics | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 1st year Master Biostatistics | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 1st year Master Data Science | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| Exchange Programme Statistics | Optional | 108 | 4,0 | 108 | 4,0 | 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. | | | - DC
| ... correctly using state-of-the-art software. | | | - DC
| ... correctly using state-of-the-art design methodology. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student knows the societal relevance of statistics 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 knows the relevant stakeholders and understands the need for assertive and empathic interaction with them. | - 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 methodologically, thus contributing to scientific research 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 application. |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
This course introduces the student to the basics of epidemiology. The focus is on the methodology of the study of the occurrence of health and disease in human (and by extent in animal) populations. The course starts with an overview of the history and present of epidemiology. Then, it introduces the measures of occurrence and association, and the concepts of causality and systematic error. Last, the main study designs used in epidemiology are discussed. In particular, the general concepts in relation to cohort, case-control and trials are explained.
By the end of this course, students should be able to:
- understand basic terminology and basic principles of epidemiology,
- calculate and interpret the different measures of occurrence, association and impact used in epidemiological research
- understand causality concepts and identify threats to causality in epidemiological studies (bias)
- accurately present different types of epidemiological studies.
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Self-study assignment ✔
|
|
|
|
Period 2 Credits 4,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 40 % |
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|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Compulsory course material |
| |
Hand-outs, slides, selected readings |
|
|
| Recommended reading |
| |
- Epidemiology. An introduction,Kenneth J. Rothman,2,Oxford University Press,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=5763681&pq-origsite=summon
- Epidemiology by Design,Daniel Westreich,1,Oxford University Press
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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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