Abstract : Stochastic differential equations with mixed effects provide means to model intraindividual and in-terindividual variability in biomedical experiments based on longitudinal data. We consider N i.i.d. stochastic processes (Xi(t), t ∈ [0, T ]), i = 1,. .. , N , defined by a stochastic differential equation with linear mixed effects. We consider a parametric framework with distributions leading to explicit approximate likelihood functions and investigate the asymptotic behaviour of estimators under the double asymptotic framework: the number N of individuals (trajectories) and the number n of observations per individual tend to infinity within the fixed time interval [0, T ]. The estimation method is assessed on simulated data for various models comprised in our framework.