This information sheet indicates how the course will be organized at pandemic code level yellow and green.
If the colour codes change during the academic year to orange or red, modifications are possible, for example to the teaching and evaluation methods.

Scientific Training 2

Course Code :1004GENGE2
Study domain:Medicine
Academic year:2020-2021
Semester:1st semester
Contact hours:52
Study load (hours):84
Contract restrictions: Faculty decision based on student file
Language of instruction:Dutch
Exam period:exam in the 1st and/or 2nd semester
Lecturer(s)Steven Abrams

3. Course contents *

General content:

  • Introduction to epidemiology and medical statistics

Content epidemiology:

  • From research question over research object to research protocol: diagnosis, prognosis and etiognosis
  • The relation between the theoretical population and the study population
  • Sampling and the consequential random error
  • Frequency of occurrence for events and states: prevalence, incidence and survival analysis
  • Diagnosis: about cut-offs and prevalence functions
  • Prognosis: interventional and descriptive prognostic research
  • Etiognosis: causal functions
  • Sources of error in medical scientific research 
  • Confounding and effect modification

Content medical statistics:

  • Descriptive statistics: the presentation of results
  • Inferential statistics: the reporting of simple and multiple associations
    • Simple and multiple linear regression
    • Multiple logistic regression
    • Cox proportional hazards regression

Aims medical statistics:

  • The student is able to apply simple statistical techniques to datasets. 
  • The student is able to apply definitions and mathematical formulas to solve probabilistic problems.
  • The student is able to perform elementary statistical analyses with statistical R software.
  • The student is able to interpret statistical output.
  • The student is able to report about statistical analyses.
  • The student is able to judge medical information/literature on validity and is able to assess critically whether the applied statistical methodology is appropriate, i.a. using principles from inferential statistics.