Parameter estimation in non-linear mixed effects models with SAEM algorithm: extension from ODE to PDE

We simulate data sets using the bi-exponential model for HIV dynamics proposed by Ding and Wu (2001), and evaluate the statistical properties of the extended SAEM parameter

Note that generating random samples from p i (ψ i |y i ) is useful for several tasks to avoid approximation of the model, such as linearisation or Laplace method. Such tasks include

Test for 92 populations of 1000 individuals: Results (from Monolix) and errors for the mean parameters of the population.. Column E1 refers to a population

L’enfant diabétique est exposé à deux complications métaboliques aiguës (hypoglycémies ou hyperglycémies avec acidocétose), pouvant être à l’origine d’un

In summarizing time-to-event data, there are two main functions of interest, namely the survival function and the hazard function. The actual event time t can be regarded as the

Extension of the EM-algorithm using PLS to fit linear mixed effects models for high dimensional repeated dataI. Caroline Bazzoli, Sophie Lambert-Lacroix,

Since the individual parameters inside the NLMEM are not observed, we propose to combine the EM al- gorithm usually used for mixtures models when the mixture structure concerns

Abstract An ODE model introduced by Gyllenberg and Webb (1989) de- scribes tumour growth in terms of the dynamics between proliferating and quiescent cell states.. The passage from