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Equipe BioStatistique-Santé (BSS) Pascal ROY PU-PH

Equipe BioStatistique-Santé (BSS) Pascal ROY PU-PH. AXES DE RECHERCHE. Nadine Bossard Michel Cucherat René Ecochard Muriel Rabilloud Pascal Roy. Incidence Prévalence Survie. Ingénierie des connaissances. Variabilité populationnelle, Biologique et Erreur de mesure.

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Equipe BioStatistique-Santé (BSS) Pascal ROY PU-PH

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  1. Equipe BioStatistique-Santé (BSS)Pascal ROY PU-PH

  2. AXES DE RECHERCHE Nadine Bossard Michel Cucherat René Ecochard Muriel Rabilloud Pascal Roy Incidence Prévalence Survie Ingénierie des connaissances Variabilité populationnelle, Biologique et Erreur de mesure Méthodes d’inférence dans l’analyse de la décision médicale Mesures de distances

  3. -1- Incidence / Prévalence /Survie Approche clinique • Modélisation pronostique • Patients à haut risque • Adaptation du traitement • Evaluation des thérapeutiques

  4. Follicular Lymphoma International Prognostic Index(age, Aastage, Hb level, LDH, Nb nodal sites) Follicular lymphoma international prognostic index. Blood 2004; (104): 1258-1265.

  5. PSS EORTC/GELA GSHG NCIC/ECOG

  6. Perspectives • Evaluer les propriétés prédictives des modèles • Estimer la part du pronostic attribuable • aux caractéristiques cliniques et biologiques classiques • aux caractéristiques transcriptomique ou protéomique des tumeurs

  7. -1- Incidence / Prévalence /Survie Approche épidémiologique • Estimation de l’incidence et de la survie du cancer en France. Cancer incidence and mortality in France over the period 1978-2000. Rev Epidemiol Sante Publique 2003; (51): 3-30. Evolution de l'incidence et de la mortalité par cancer en France de 1978 à 2000. INVS, 1-217. 2003. Ref Type: Serial (Book,Monograph) • Estimation of relative survival in cancer patients The FRANCIM population based study

  8. Statistical methodsExcess rate model (1) For each subject, mortality rate at time t has two components x = age at diagnosis Calculated for age at exit (=x+t) Z= vector of explanatory covariates Potentially: sex, département, year of diagnosis, age at diagnosis…. Z1 = vector of the 3 covariates defining expected mortality rates: sex , département ,year of death

  9. Excess Rate model (2) with f(t) being constant within intervals defined a priori A smoothed parametric function Cubic spline with 1 knot  5 parameters 10 intervals  10 parameters Excess rate Excess rate

  10. Estimating the model parameters Maximum Likelihood Estimation (MLE) If f(t) is a step function: The survival likelihood is equal to a Poisson likelihood, up to a constant. Making feasible the estimation of ML in the framework of generalized linear models, With any computer software where a weighted least squares is available  This was implemented in Splus (Iwls)

  11. If f(t) is a smoothed parametric function MLE is directly applicable: approximateLc with a numerical integration method (Simpson) IWLSis not directly applicable:  an « appropriate » time tifor the calculation of the likelihood has to be choosen: Time at exit if d is 1 / Mid-point of the interval if d is 0

  12.  Estimating of relative survival at fixed times (1..3..5 years..)  Need for an « optimal » modeling of the excess rate being able to deal with sparse data (stability of estimates) • With f(t) being a smoothed parametric function • on the whole follow-up time (up to 10 years) • Selection of the « best » function (AIC) among: Polynomial up to cubic, cubic spline with 1 or 2 knots.

  13. Estimating the proper effects of covariates Need for a multivariate use of the model With an optimal modelling of the effect of covariates Especially : age at diagnosis

  14. Example (1)Kidney cancerMultivariate analysis

  15. Large effect of age at early follow up Small effect of age at late follow up

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