Building data analysis skills with Linear Mixed Models

In September 2026, ViroiDoc doctoral candidates strengthened their skills in advanced statistical methods for analysing complex bioscience data.

Modern bioscience research often involves complex datasets in which observations are grouped, repeated or otherwise interconnected. Linear Mixed Models (LMMs) provide powerful tools for analysing such data while accounting for these underlying structures. To help doctoral candidates develop practical skills in this area, ViroiDoc organised a four-day hybrid training course from 7 to 10 September 2026 as part of its transferable skills programme.

The course was organised by the University of Ljubljana, Faculty of Medicine, in cooperation with the Biotechnical Faculty, and led by Rok Blagus and Lan Gerdej. Combining online and in-person sessions, the training gave participants hands-on experience in preparing data, fitting mixed-effects models in R, interpreting model outputs and assessing key model assumptions.

The programme covered essential concepts including fixed and random effects, modelling of hierarchical and clustered data, and extensions to Generalized Linear Mixed Models (GLMMs). Participants also explored alternative approaches to mixed-effects modelling, broadening their understanding of statistical methods that can be applied to a range of bioscience research questions.

By combining methodological knowledge with practical experience, the training supports ViroiDoc’s goal of equipping doctoral candidates with advanced research and transferable skills for their future careers. Activities such as this also provide opportunities for knowledge exchange across disciplines and contribute to a strong, collaborative research environment within the ViroiDoc network.

This English-language training was free of charge for selected participants who have registered. It was part of the EU-funded ViroiDoc project.