Regression models in medical research

Study facts

Course codeMEDSTA2
ECTS credits5
Teaching beginsSpring
Number of semesters1
Teaching language
Norwegian. English if international PhD candidates.
Study levelPostgraduate Courses
Resources
Belongs toDepartment of Global Public Health and Primary Care

Contact

Contact Information

Department of Global Public Health and Primary Care

e-mail: studie@igs.uib.no

Course coordinator: Rolv Terje Lie

e-mail: rolv.lie@igs.uib.no

Aim 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

Knowledge

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

Skills

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

.

Course offered (semester)

Spring

Course Unit Level

Master/PhD -level

Pre-requirements

HELSTA/MEDSTA or equivalent. Knowledge about statistical packages

Teaching Methods

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 Requirements

Two mandatory home assignments.

One group presentation

Assessment methods

Written home exam.

Grading Scale

Pass/Fail