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Postgraduate course

Regression models in medical research

  • ECTS credits5
  • Teaching semesterSpring
  • Course codeMEDSTA2
  • Number of semesters1
  • LanguageNorwegian. English if international PhD candidates.
  • Resources

Main content

Level of Study

Master/PhD -level

Teaching semester


Objectives and Content

The aim of the course is to give an introduction to regression models that are used in medical and epidemiological research (Cox regression, logistic regression, linear (Gaussian) regression etc.). The course will give an understanding of applications and limitations of the methods. Furthermore, the students will learn to perform simple analyses, interpret output from statistical packages and present such results in scientific articles.

Learning Outcomes


After completing the course, the students should be able to

  • Understand how basic regression models (linear, logistic, Cox etc.) work
  • Understand how a regression model is built using different variables, and be able to interpret regression coefficients
  • Understand how effect size, significance level, and sample size affect statistical power


After completing the course, the students should be able to

  • perform and interpret basic regression analyses

General competence

After completing the course, the students should

have acquired good scientific practice and understanding of how to report data and numbers, for example not report selectively or exaggerate own results and their implications


Required Previous Knowledge

HELSTA/MEDSTA or equivalent. Knowledge about statistical packages

Teaching Methods and Extent of Organized Teaching

Lectures and practices.

6 days of teaching.

The exercises will be based on the program Stata.

NB! All students must bring their own computer with Stata installed!

Compulsory Assignments and Attendance

Two mandatory home assignments.

Forms of Assessment

Written home exam.

Grading Scale



Department of Global Public Health and Primary Care

e-mail: studie@igs.uib.no

Course coordinator: Magne Haugland Solheim


Exam information

  • Type of assessment: Take-home examination

    Assignment handed out
    10.03.2022, 15:00
    Submission deadline
    28.03.2022, 23:59
    Withdrawal deadline
    Examination system
    Digital exam