Mixed Models– Analyzing data with repeated measurements or clusters

The course address datasets containing multiple measurements of the same individuals or of groups, a situation in which classical statistical approaches are biased. In this course, we begin with a summary of linear models and their limitations, then explain “Mixed Models”, their applicability, and usage. We will cover random intercept and random slope models in detail. Besides discussions on the interpretation and theory also ideas how to run the models with R and exercises will be provided.

Topics

• Limits of linear regression in case of repeated measurements
• Introduction of mixed models
• Random intercept models
• Random slope models
• Interpretation and application of mixed models using R

Methods
The course consists of theoretical lessons on mixed models, how to apply and how to interpret mixed models. Theoretical lessons will be followed by hands-on examples with best-practice solutions in R.

Prerequisites
Programming skills with R and knowledge of regression models. Some practice in ggplot2 is also welcome.

The course will be taught be staff of the Core Facility Statistical Consulting at Helmholtz Munich. All trainers of the Core Facility Statistical Consultant are trained statisticians with years of experience in teaching statistics and programming courses.