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Module guide

Module MA2600-KP04, MA2600

Biostatistics 2 (BioStat2)

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
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
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
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.
Grading through:
  • written exam
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
Language:
  • offered only in German
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