Section: New Results

Model-based clustering for pharmacovigilance data

Participants : Gilles Celeux, Christine Keribin, Valérie Robert.

In collaboration with Pascale Tubert-Bitter, Ismael Ahmed and Mohamed Sedki, Gilles Celeux and Christine Keribin worked on the detection of associations between drugs and adverse events in the framework of the PhD of Valerie Robert, which was defended this year. At first, this team developed model-based clustering inspired by latent block models (LBMs), which consists of co-clustering rows and columns of two binary tables, imposing the same row ranking. This enabled it to highlight subgroups of individuals sharing the same drug profile, and subgroups of adverse effects and drugs with strong interactions. Furthermore, some sufficient conditions are provided to obtain identifiability of the model, and some results are shown for simulated data. The exact ICL criterion has been extended to this double block latent model. Through computer experiments, Valérie Robert demonstrated the interest of the proposed model, compared with standard contingency table analysis, to detect co-prescription and masking effects.

Futhermore, with V. Robert, C. Kerebin and G. Celeux showed that it can be useful to use an LBM model on a contingency table of drugs and adverse effects to do cluster initialization for dealing with individual's data.