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

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

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

3 3 1.Modélisation pronostique 1.Patients à haut risque 2.Adaptation du traitement 3.Evaluation des thérapeutiques -1- Incidence / Prévalence /Survie Approche clinique

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

5 5 PSS EORTC/GELA GSHG NCIC/ECOG

6 6 Lymphome de Hodgkins StagesN° Patients (%)EFS % (SE)DCVLHR (95% CI) PSS Early Intermediate Advanced 465 (48.7) 329 (34.4) 161 (16.9) 92.7 (1.2) 80.2 (2.2) 59.0 (3.9) ** ( ) 7.2 ** ( ) EORTC/GELA Early Intermediate Advanced 299 (31.3) 356 (37.3) 300 (31.4) 92.6 (1.5) 86.8 (1.8) 68.0 (2.7) * ( ) 5.1 ** ( ) GSHG Early Intermediate Advanced 270 (28.3) 318 (33.3) 367 (38.4) 91.1 (1.7) 89.0 (1.7) 71.1 (2.4) ( ) 3.7 ** ( ) NCIC/ECOG Early Intermediate Advanced 205 (21.5) 271 (28.4) 479 (50.1) 93.7 (1.7) 89.3 (1.9) 74.3 (2.0) ( ) 4.6 ** ( )

7 7 1.Evaluer les propriétés prédictives des modèles 2.Estimer la part du pronostic attribuable 1.aux caractéristiques cliniques et biologiques classiques 2.aux caractéristiques transcriptomique ou protéomique des tumeurs Perspectives

8 8 1.Estimation de lincidence et de la survie du cancer en France. Cancer incidence and mortality in France over the period Rev Epidemiol Sante Publique 2003; (51): Evolution de l'incidence et de la mortalité par cancer en France de 1978 à INVS, Ref Type: Serial (Book,Monograph) 2.Estimation of relative survival in cancer patients The FRANCIM population based study -1- Incidence / Prévalence /Survie Approche épidémiologique

9 9 Statistical methods Excess rate model (1) For each subject, mortality rate at time t has two components Z= vector of explanatory covariates Potentially: sex, département, year of diagnosis, age at diagnosis…. Z 1 = vector of the 3 covariates defining expected mortality rates: sex, département,year of death x = age at diagnosis Calculated for age at exit (=x+t)

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

11 11 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)

12 12 If f(t) is a smoothed parametric function MLE is directly applicable: approximate c with a numerical integration method (Simpson) IWLS is not directly applicable: an « appropriate » time t i for the calculation of the likelihood has to be choosen: Time at exit if is 1 / Mid-point of the interval if is 0

13 13

14 14 Estimating of relative survival at fixed times ( years..) 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. Need for an « optimal » modeling of the excess rate being able to deal with sparse data (stability of estimates)

15 15 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

16 16 Example (1) Kidney cancer Multivariate analysis

17 17

18 18 Large effect of age at early follow up Small effect of age at late follow up


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