Course content

Advantages and problems with dependent observations. Summary measures (e.g. Area under the curve [AUC], slope coefficient, min/max values). Covariance and correlation. Variance components. Linear mixed effects models. Logistic mixed effects models. Use of relevant software (Stata)

Learning outcome

After completing the course, the student should be able to:

• Understand the nature of dependency in clustered measurements, and how this dependency alters the approach to statistical analysis and modeling; as well as the consequences of ignoring it;
• Identify clusters and potential dependency by inspecting the design and/or viewing the resulting data set;
• Understand the principles of experimental design in which experimental factors vary both between and within clusters;
• Perform simple, descriptive analyses such as obtaining sample covariance and correlation, and corresponding scatter plots to illustrate key features of data with clustered observations;
• Estimate and interpret variance components;
• Estimate and interpret linear mixed regression models and logistic mixed effects models with random intercept;
• Estimate and interpret linear mixed effects models with random intercept and random slopes for covariates;
• Use of relevant software

Learning methods and activities

Forelesninger og øvinger med veiledning

Recommended previous knowledge

Basic medical statistics

Linear regression

Course materials

Textbook: Rabe-Hesketh & Skrondal (2021). Multilevel and Longitudinal Modeling Using Stata; Volume 1 : Continuous Responses, Volume 2: Categorical Responses, Counts, and Survival. Fourth Edition, Stata Press. College Station, USA. ISBN-13: 978-1-59718-136-5

Lecture notes, to be announced.

Credit reductions