Personnel de l'université

Yohann FOUCHER

Maître de Conférences (MCU-HDR) en Biostatistique.

Coordonnées

Institut de Recherche en Santé 2 22 boulevard Bénoni Goullin 44200 Nantes

Tél
0253009122 (n° interne : 329122)
Mail
Yohann.Foucher@univ-nantes.fr
Site internet
https://fr.linkedin.com/in/yohann-foucher-1a8055114

Activités / CV

Thèmes de recherche :
  • Analyses de données longitudinales et censurés;
  • Modèles à risques compétitifs et multi-états;
  • Méthodes pour la médecine pronostique et diagnostique;
  • Prise en compte des préférences des patients en médecine personnalisée et stratifiée;
  • Méthodes d'ajustement et scores de propension.
Diplômes universitaires:
  • 2013 : Habilitation à Diriger des Recherches. Université de Nantes. Modélisation et pronostic de l'évolution de pathologies chroniques : applications en transplantation rénale.
  • 2007 : Thèse de doctorat de l'Université Montpellier 1. Modèles semi-markoviens : Application à l'analyse de l'évolution de pathologies chroniques.
Brevets:
  • KTFS. Ce score, calculé à un an post transplantation, permet d'évaluer le risque d'échec de greffe (retour en dialyse) jusqu'au 8ème anniversaire de la greffe. Numéro d'enregistrement: 0959043. Titre : Method and device for determining a risk of graft rejection.
  • DGFS. Ce score, calculé en post-greffe, permet d'évaluer le risque de retard au démarrage du greffon. Numéro d'enregistrement: EP13196554. Titre : Method for predicting delayed graft function in kidney transplantation.

Principales publications internationales depuis 2010 (premier ou dernier auteur):

  • Le Borgne F., Giraudeau B., Quérard A.H., Giral M., Foucher Y. (2015) Comparisons of the performance of different statistical tests for time-to-event analysis with confounding factors: practical illustrations in kidney transplantation. Statistics in Medicine. 30;35(7):1103-16.
  • Gillaizeau F., Dantan E., Giral M., Foucher Y. (2015) A multistate additive relative survivalsemi-Markov model. Statistical Methods in Medical Research: online first.
  • Lorent M., Giral M., Foucher Y. (2014) Net time-dependent ROC curves: a solution for evaluating the accuracy of a marker to predict disease-related mortality. Statistics in Medicine,33(14): 2379-89.
  • Chapal M., Le Borgne F., Legendre C., Kreiss H., Mourad G., Garrigue V., Morelon E.,Buron F., Rostaing L., Kamar N., Kessler M., Ladrière M., Soulillou J.P.,Launay K., Daguin P., Offredo L., Giral M., Foucher Y. (2014) A useful scoring system for the prediction andmanagement of delayed graft function following kidney transplantation fromcadaveric donors. Kidney International, 86(6):1130-9.
  • Dantan E., Combescure C., Lorent M., Ashton-Chess J., Daguin P., Classe J.M., GiralM., Foucher Y. (2014) An original approach was used to better evaluate the capacity of a prognostic marker usingpublished survival curves. Journal of Clinical Epidemiology, 67(4): 441-8.
  • Foucher Y., Akl A., Rousseau V.,Trébern-Launay K., Lorent M., Kessler M., Ladrière M., Legendre C., Kreiss H.,Rostaing L., Kamar N., Mourad G., Garrigue V., Morelon E., Daurès J.P.,Soulillou J.P., Giral M. (2014) An alternative approach to estimate age-related mortality of kidney transplant recipients compared to the general population: results in favor of old-to-old transplantations. Transplant International, 27(2): 219-25.
  • Combescure C., Perneger T., Weber D., Daurès J.P., FoucherY. (2014) Prognostic ROC Curves: A Method for Representing the Overall Discriminative Capacity of Binary Markers with Right-Censored Time-to-Event Endpoints. Epidemiology, 25(1): 103-9.
  • Trebern-Launay K., Giral M., Dantal J., Foucher Y. (2013) Comparison of the risk factors effects between two populations: twoalternative approaches illustrated by the analysis of first and second kidney transplant recipients. BMC Medical Research Methodology, 13: 102.
  • Danger R., Foucher Y. (2012) Time dependent ROC curves for the estimation of true prognostic capacity of microarray data. Statistical Applications in Genetics and Molecular Biology, 11(6)
  • Combescure C., Daurès J.P., Foucher Y. (2012) A literature-based approach to evaluate the predictive capacity of a marker usingtime-dependent summary receiver operating characteristics. Stat Methods Med Res: online first.
  • Foucher Y., Combescure C., Ashton-ChessJ., Giral M. (2012) Prognostic Markers: Data Misinterpretation Often Leads toOveroptimistic Conclusions. Am J Transplant, 12(4): 1060-1.
  • Foucher Y., Giral M., Soulillou J.P.,Daurès J.P. (2012) Cut-off estimation and medical decision making based on acontinuous prognostic factor : the prediction of kidney graft failure. IntJ Biostat, 8(1): 1-13.
  • Foucher Y., Giral M., Soulillou J.P., Daurès J.P. (2010) Time-dependent ROC analysis for a three-class prognosticwith application to kidney transplantation. Statisticsin Medicine, 29(30): 3079-87.
  • Foucher Y., Daguin P., Akl A., KesslerM., Ladrière M., Legendre C., Kreiss H., Kamar N., Rostaing L., Garrigue V.,Bayle F., de Ligny B., Buchler M., Meier C., Soulillou J.P., Giral M. (2010) Aclinical scoring system highly predictive of long-term kidney graft survival.. KidneyInternational, 78(12): 1288-94.
  • Foucher Y., Giral M., Soulillou J.P., Daurès J.P. (2010) A flexible semi-Markov model for interval-censored data andgoodness-of-fit testing. Statistical Methods in Medical Research, 19(2): 127-45.
  • Foucher Y., Giral M., Soulillou J.P., Daurès J.P. (2010) Cut-off estimation and medical decision making based on a continuous prognostic factor: the prediction of kidney graft failure. InternationalJournal of Biostatistics, 8(1).