As a Ph.D. candidate in Biostatistics, I have a strong focus on personalized medicine. My research involves investigating the methodology for estimating individual treatment effects (ITE). My work encompasses the examination of the performance of various statistical and machine learning techniques for the ITE estimation, utilizing data from single randomized controlled trials (RCTs) as well as data from multiple RCTs through individual participant data meta-analysis.
Research interests
- Personalized medicine
- Methods for individual treatment rules and effects (i.e., ITR/ITE)
- Predictive analytics
Key publications
- Bouvier, F. B., & Porcher, R. (2023). What should be done and what should be avoided when comparing two treatments?. Best Practice & Research Clinical Haematology, 36(2), 101473.
View publication - Bouvier F, Peyrot E, Balendran A, et al. Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data. Statistics in Medicine. 2024; 1-19. doi: 10.1002/sim.10059
View publication - Bouvier, F., Chaimani, A., Peyrot, E., Gueyffier, F., Grenet, G., & Porcher, R. (2024). Estimating individualized treatment effects using an individual participant data meta-analysis. BMC Medical Research Methodology, 24(1), 74.
View publication
Links
Theses
Estimation of individualized treatment effects using an individual participant data meta-analysis (temporary title)
Florie Bouvier
Promotion : 2020
Supervisor.s : Raphaël Porcher
