Analysis of longitudinal data is essential for understanding the evolution of chronic diseases, including neurological and psychiatric diseases, their variability among individuals, and the response to treatment. This two-day workshop, organized by the ARAMIS team as part of the Open Brain School, will address the main methodological challenges related to longitudinal data: irregular follow-ups, missing data, repeated measures, and multimodal data from cohorts or clinical trials.
The first day will be devoted to presentations by international experts on methods, applications and issues related to longitudinal data. The second day will offer practical tutorials around open-source software in R and Python, including Leaspy, JMbayes2, lcmm and saemix.
This workshop is intended for doctoral students, post-doctoral fellows, engineers, researchers and clinicians interested in improving the analysis, interpretation and reproducibility of longitudinal and repeated measures data.
Programme
Day 1 – Thursday September 3, 2026
Expert lectures and presentations on complex data, epidemiology applications and imaging studies. The detailed programme will be available shortly.
Day 2 – Friday, September 4, 2026
Practical tutorials on open-source software in R and Python. Participants will be able to choose two of the four sessions:
- Disease Progression Modeling with Leaspy;
- Joint Models for Longitudinal and Time-to-Event Data with JMbayes2;
- Latent Class Mixed Modeling with lcmm;
- Non-linear Mixed Effect Models with saemix.
Place and facility
Presented at the Institut du Cerveau / Paris Brain Institute and the Data & AI Center
Location indicated on the registration platform: Brain Institute, main site
Awards
30€ for the first day, 60€ for both days including lunch.
Organising Committee
Sophie Tezenas du Montcel, PI
Sofia Kaisaridi, postdoctoral researcher
Sebastian Mendez, research engineer
Gabrielle Casimiro, PhD student
Maylis Tran, PhD student
The ARAMIS team, led by Ninon BURGOS & Olivier COLLIOT, aims to build numerical models of brain diseases, particularly neurodegenerative pathologies, from multimodal patient databases. The main approaches used are machine learning (artificial...
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