Duration:
1 Semester | Turnus of offer:
each summer semester | Credit points:
4 |
Course of studies, specific field and terms: - Master Medical Informatics 2019 (optional subject), Medical Data Science / Artificial Intelligence, 1st or 2nd semester
- Master Biophysics 2019 (optional subject), Elective, 2nd semester
- Master Medical Informatics 2014 (optional subject), ehealth / infomatics, 1st or 2nd semester
- Master Computer Science 2012 (optional subject), specialization field medical informatics, 3rd semester
- Master Computer Science 2012 (optional subject), specialization field bioinformatics, 2nd or 3rd semester
- Master Computer Science 2012 (optional subject), advanced curriculum stochastics, 2nd semester
- Bachelor CLS 2010 (compulsory), mathematics, 4th semester
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Classes and lectures: - Biostatistics 2 (exercise, 1 SWS)
- Biostatistics 2 (lecture, 2 SWS)
| Workload: - 45 Hours in-classroom work
- 25 Hours programming
- 15 Hours exam preparation
- 35 Hours private studies
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Contents of teaching: | - Knowledge of model assumptions and mathematical foundation of model assumptions for the linear model
- Knowledge of possible sources of errors in the modelling
- Competence in independent analysis of a study using the linear model
- Competence in correctly interpreting study results
- Competence in parameter interpretation and regression diagnostics
- Knowledge of model assumptions and mathematical foundation of the generalized linear model
- Competence in the independent analysis of a simple study with a dichotomous outcome
- Competence in correctly interpreting study results of a study with a dichotomous outcome
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Qualification-goals/Competencies: - The students are able to enumerate and explain the assumptions of the classical linear model.
- The students are able to describe typical applications of the classical linear model.
- The students are able to list the differences between the linear model and the logistic regression model.
- The students are able to describe possible error sources in modelling the linear model.
- The students are able to calculate the estimators (point and interval estimators, residual) in the linear model by hand.
- The students are able to evaluate the graphics for regression diagnostics in the linear model.
- The students are able to interpret the results of studies, where a linear, a logistic or a Cox regression model was applied.
- The students are able to draw and interpret Kaplan-Meier curves.
- The students are able to perform data transformations.
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Grading through: |
Is requisite for: |
Requires: |
Responsible for this module: - Prof. Dr. rer. biol. hum. Inke König
Teachers: |
Literature: - Ludwig Fahrmeir, Thomas Kneib, Stefan Lang: Regression: Modelle, Methoden und Anwendungen - ISBN-13 9783540339328
- Dobson, Annette J & Barnett, Adrian: An Introduction to Generalized Linear Models, 3rd ed. - Chapman & Hall/CRC: Boca Raton (FL), 2008
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Language: |
Notes:Prerequisites for attending the module:
- None (The competences of the required modules are required for this module, but the modules are not a prerequisite for admission.)
Prerequisites for the exam:
- Preliminary examinations can be determined at the beginning of the semester. If preliminary work has been defined, it must have been completed and positively assessed before the initial examination. |
Letzte Änderung: 21.1.2020 |
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