Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects.

ABSTRACT: This article focuses on parameter estimation of multilevel nonlinear mixed-effects models (MNLMEMs). These models are used to analyze data presenting multiple hierarchical levels of grouping (cluster data, clinical trials with several observation periods, ...). The variability of the individual parameters of the regression function is thus decomposed as a between-subject variability and higher levels of variability (e.g. within-subject variability). We propose maximum likelihood estimates of parameters of those MNLMEMs with 2 levels of random effects, using an extension of the stochastic approximation version of expectation-maximization (SAEM)-Monte Carlo Markov chain algorithm. The extended SAEM algorithm is split into an explicit direct expectation-maximization (EM) algorithm and a stoch

SUBMITTER: Panhard X 

PROVIDER: S-EPMC2722900 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature