| 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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Following programme components must have been included in your study programme in a previous education period
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Concepts of Probability and Statistics DL (3220)
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5.0 stptn |
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Generalized Linear Models DL (3580)
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6.0 stptn |
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Biostatistics - distance learning | 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 has the habit to assess data quality and integrity. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | - 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 statistical and data science.
| | | - 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 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The course provides an introduction to the survival analysis.
Topics: basics (censoring mechanisms, characteristics of the time-to-failure distribution, etc.); basic time to failure distributions (exponential, Weibull); Kaplan Meier estimator; tests for comparing of survival curves (logrank, Gehans, logrank test for trend, extensions); proportional hazards model (estimation, diagnostics); parameteric models; marginal models for multivariate and correlated failure-time data; competing risks.
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Collective feedback moment ✔
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Distance learning ✔
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Homework ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 25 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Other evaluation method during teaching period | 5 % |
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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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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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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 quizzes and 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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Leture notes and reading materials provided by the instructor |
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| Recommended reading |
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- Modeling Survival Data in Medical Research,Collett D,2,Chapman and Hall/CRC,9781584883258,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=5345205&pq-origsite=summon
- Modeling Survival Data: Extending the Cox Model,Terry M. Therneau Patricia M. Grambsch,9781441931610
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| second year Quantitative Epidemiology - distance learning | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | No | 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 has the habit to assess data quality and integrity. | - 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 is an effective written and oral communicator, both within their own field as well as across disciplines. | - 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 statistical and data science.
| | | - 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 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 provides an introduction to the survival analysis.
Topics: basics (censoring mechanisms, characteristics of the time-to-failure distribution, etc.); basic time to failure distributions (exponential, Weibull); Kaplan Meier estimator; tests for comparing of survival curves (logrank, Gehans, logrank test for trend, extensions); proportional hazards model (estimation, diagnostics); parameteric models; marginal models for multivariate and correlated failure-time data; competing risks.
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|
|
Collective feedback moment ✔
|
|
|
|
Distance learning ✔
|
|
|
|
|
|
|
|
Homework ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 25 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
| Other evaluation method during teaching period | 5 % |
|
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
| Written exam | 70 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
| Off campus online evaluation/exam | ✔ |
|
| For the full evaluation/exam | ✔ |
|
|
|
| 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 quizzes and homework scores are retained when computing the final score after the second chance exam. |
|
|
|
| Compulsory course material |
| |
Leture notes and reading materials provided by the instructor |
|
|
| Recommended reading |
| |
- Modeling Survival Data in Medical Research,Collett D,2,Chapman and Hall/CRC,9781584883258,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=5345205&pq-origsite=summon
- Modeling Survival Data: Extending the Cox Model,Terry M. Therneau Patricia M. Grambsch,9781441931610
|
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| second year Master Bioinformatics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Data Science - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
|
| | | 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 has the habit to assess data quality and integrity. | - EC
| The student knows the international nature of the field of statistical science and data science. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | - 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 statistical and data science.
| | | - 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 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 provides an introduction to the survival analysis.
Topics: basics (censoring mechanisms, characteristics of the time-to-failure distribution, etc.); basic time to failure distributions (exponential, Weibull); Kaplan Meier estimator; tests for comparing of survival curves (logrank, Gehans, logrank test for trend, extensions); proportional hazards model (estimation, diagnostics); parameteric models; marginal models for multivariate and correlated failure-time data; competing risks.
|
|
|
|
|
|
|
|
|
Collective feedback moment ✔
|
|
|
|
Distance learning ✔
|
|
|
|
|
|
|
|
Homework ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 25 % |
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
| Other evaluation method during teaching period | 5 % |
|
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
| Written exam | 70 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
| Off campus online evaluation/exam | ✔ |
|
| For the full evaluation/exam | ✔ |
|
|
|
| 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 quizzes and homework scores are retained when computing the final score after the second chance exam. |
|
|
|
| Compulsory course material |
| |
Leture notes and reading materials provided by the instructor |
|
|
| Recommended reading |
| |
- Modeling Survival Data in Medical Research,Collett D,2,Chapman and Hall/CRC,9781584883258,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=5345205&pq-origsite=summon
- Modeling Survival Data: Extending the Cox Model,Terry M. Therneau Patricia M. Grambsch,9781441931610
|
|
|
|
|
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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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