France Mentré (INSERM)

Nonlinear mixed effects models for the analysis and design of bioequivalence/biosimilarity studies

vendredi 3 mai 2013, 11h00 - 12h00

Salle de réunion, espace Turing


Nonlinear mixed effects models (NLMEM) can be used to analyze crossover pharmacokinetic bioequivalence or biosimilarity studies, as an alternative to standard non-compartmental analysis especially for trial in patients (1). Data should be modeled in one step, with both inter and intra patient random effects, using an appropriate estimation method, like the SAEM algorithm implemented in the software MONOLIX. We extended Wald and likelihood ratio tests of treatment effect to test equivalence and showed their properties on simulation (2). Before the modelling step, it is important to define an appropriate design which has an impact on the precision of parameter estimates and on the power of tests. We propose an extension of the evaluation of the Fisher Information Matrix for NLMEM including within subject variability in addition to between subject variability using a first order expansion of the model. We also include fixed effects for covariates like treatment, period and sequence usually tested in these crossover trials. We use the predicted standard errors to predict the power of the Wald test for difference or for bioequivalence and to compute the number of subject needed (3). These extensions are implemented in the newly released version PFIM 3.2 and were evaluated by simulations (4).


-# Dubois A, Gsteiger S, Balser S, Pigeolet E, Steimer JL, Pillai C, Mentré F. Analysis of pharmacokinetic similarity of biologics using nonlinear mixed effects modelling. Clinical Pharmacology and Therapeutics, 2012, 91:234-242.
-# Dubois A, Lavielle M, Gsteiger S, Piegeolet E, Mentré F. Model-based analyses of bioequivalence crossover trials using the SAEM algorithm. Statistics in Medicine, 2011, 30:2582-2600.
-# Nguyen TT, Bazzoli C, Mentré F. Design evaluation and optimisation in crossover pharmacokinetic studies analysed by nonlinear mixed effects models. Statistics in Medicine, 2012, 20:1043-1058.
-# Bazzoli C, Retout S, Mentré F. Design evaluation and optimisation in multiple response nonlinear mixed effect models: PFIM 3.0. Computer Methods and Programs in Biomedicine, 2010, 98:55-65.


France Mentré, Anne Dubois, Thu-Thuy Nguyen, Caroline Bazzoli


Exposé en français