Date de l'évènement :
à 12:00, (Paris, Europe)
Lieu de l'évènement :
En ligne
Organisé par :
Professeur Roch GIORGI - Equipe QuanTIM-SESSTIM
Intervenant :
Yohann FOUCHER
Université de Poitiers, CHU de Poitiers, CIC Inserm 1402 - Axe Scale-Epi (methodS in ClinicAL rEsearch and EPIdemiology)
Description :
In clinical practice, it is often useful to predict the probability that a subject will experience an event. A Super Learner makes it possible to combine different regression models and algorithms. We have proposed the R package “survivalSL”, which contains various functions for building a Super Learner in the presence of censored event-time data. Compared with existing solutions, we provide a large number of learners and loss functions. We conducted simulations to describe the performance of our approach and also illustrated its use in an application to multiple sclerosis.
Keywords: Censored data, prediction, survival, machine learning, super learning
