R&D engineer at IDBC since 2013 first as part of a CIFRE thesis then as part of LabCom RISCA (public-private partnership between the company IDBC and the SPHERE team, www.labcom-risca.com) . I am responsible for the development of Plug-Stat, a statistical analysis software for health data (cohorts, registers). My areas of research relate to the analysis of observational data (causal inference, machine learning, time-dependent ROC curves).
Award of Excellence for poster communication - Plug-Stat: a new statistical software tailor-made for better valorisation of cohort data (EPICLIN)
G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes.
Le Borgne F, Chatton A, Léger M, Lenain R, Foucher Y.
2021 Jan 14;11(1):1435.
Standardized and weighted time-dependent receiver operating characteristic curves to evaluate the intrinsic prognostic capacities of a marker by taking into account confounding factors.
Le Borgne F, Combescure C, Gillaizeau F, Giral M, Chapal M, Giraudeau B, et al.
G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study.
Chatton A, Le Borgne F, Leyrat C, Gillaizeau F, Rousseau C, Barbin L, et al.
2020 Jun 8;10(1):9219.
Lack of impact of pre-emptive deceased-donor kidney transplantation on graft outcomes: a propensity score-based study.
Foucher Y, Le Borgne F, Legendre C, Morelon E, Buron F, Girerd S, et al.
2019 May 1;34(5):886–91.
Comparisons of the performance of different statistical tests for time-to-event analysis with confounding factors: practical illustrations in kidney transplantation.
Le Borgne F, Giraudeau B, Querard AH, Giral M, Foucher Y.
2016 Mar 30;35(7):1103–16.