Presented by Sandro Leidi & James Gallagher
This course is running online, via Zoom.
Mixed models are a modern powerful data analysis tool to analyse clustered data, typically arising in studies where the levels of a factor are a random selection from a wider pool, or in the presence of a multi-level nested structure with different levels of variability.
Potential benefits of mixed models are greater generalisability of results and accommodation of missing values. In particular, mixed models have been used in clinical trials to analyse repeated measures, where measurements taken over time naturally cluster according to patient.
The course will illustrate medical and health related applications of mixed modelling, such as multi-centre trials, cross-over trials, and the analysis of repeated measures.
The course focuses on the linear mixed model, assuming normally distributed data, and on how to fit it and interpret its results.
Only essential theoretical aspects of mixed models will be summarised.