| 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 (1798)
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5.0 stptn |
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Generalized Linear Models (3563)
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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 | |
 | 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. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art 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. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student should be familiar with statistical inference and statistical (generalized linear, mixed effects) models.
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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, Gehan's, 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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Lecture ✔
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Group work ✔
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Homework ✔
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Presentation ✔
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Period 1 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 | Students get an "X" score if they do not meet the condition. |
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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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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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| Recommended course material |
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R and SAS are recommended softwares for this course. |
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| 2nd year Master Quantitative Epidemiology | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | No | Numerical | |
| 2nd year Master Quantitative Epidemiology - icp | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | No | 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. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art 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 student should be familiar with statistical inference and statistical (generalized linear, mixed effects) models.
|
|
|
|
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, Gehan's, 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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|
|
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|
|
|
Collective feedback moment ✔
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|
|
|
Distance learning ✔
|
|
|
|
Lecture ✔
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|
|
|
|
|
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Group work ✔
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|
|
Homework ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 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 | Students get an "X" score if they do not meet the condition. |
|
|
|
| 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. |
|
|
|
| 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
|
|
|
| Recommended course material |
| |
R and SAS are recommended softwares for this course. |
|
|
|
|
|
| 2nd year Master Bioinformatics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| Exchange Programme Statistics | 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. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art 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 student should be familiar with statistical inference and statistical (generalized linear, mixed effects) models.
|
|
|
|
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, Gehan's, 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 ✔
|
|
|
|
Lecture ✔
|
|
|
|
|
|
|
|
Group work ✔
|
|
|
|
Homework ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 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 | Students get an "X" score if they do not meet the condition. |
|
|
|
| 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 |
| |
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
|
|
|
| Recommended course material |
| |
R and SAS are recommended softwares for this course. |
|
|
|
|
|
| 1st Master of Business and Information Systems Engineering | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
| 2nd Master of Business and Information Systems Engineering | Optional | 108 | 4,0 | 108 | 4,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| The holder of the degree applies acquired knowledge independently. (Self-direction and entrepreneurial spirit) | - EC
| The holder of the degree communicates clearly and correctly in writing and orally, in a business and academic context, if necessary supplemented with visual support. (Communication) |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student should be familiar with statistical inference and statistical (generalized linear, mixed effects) models.
|
|
|
|
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, Gehan's, 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 ✔
|
|
|
|
Lecture ✔
|
|
|
|
|
|
|
|
Group work ✔
|
|
|
|
Homework ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 Credits 4,00
| 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 | Students get an "X" score if they do not meet the condition. |
|
|
|
| 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 |
| |
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
|
|
|
| Recommended course material |
| |
R and SAS are recommended softwares for this course. |
|
|
|
|
|
| Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics math | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| 5.4. The master of education is a domain expert SCIENCES: the EM has advanced knowledge and understanding of the domain disciplines relevant to the specific subject doctrine(s). |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student should be familiar with statistical inference and statistical (generalized linear, mixed effects) models.
|
|
|
|
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, Gehan's, 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 ✔
|
|
|
|
Lecture ✔
|
|
|
|
|
|
|
|
Group work ✔
|
|
|
|
Homework ✔
|
|
|
|
Presentation ✔
|
|
|
|
Period 1 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 | Students get an "X" score if they do not meet the condition. |
|
|
|
| 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 |
| |
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
|
|
|
| Recommended course material |
| |
R and SAS are recommended softwares for this course. |
|
|
|
|
|
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 |
|