SAMBA: a Novel Method for Fast Automatic Model Building in Nonlinear Mixed-Effects Models - Centre de mathématiques appliquées (CMAP) Accéder directement au contenu
Article Dans Une Revue CPT: Pharmacometrics and Systems Pharmacology Année : 2022

SAMBA: a Novel Method for Fast Automatic Model Building in Nonlinear Mixed-Effects Models

Résumé

The success of correctly identifying all the components of a nonlinear mixed-effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the SAMBA (Stochastic Approximation for Model Building Algorithm) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in 'learning something' about the 'best model', even when a 'poor model' is used to fit the data. A comparison study of the SAMBA procedure with SCM and COSSAC show similar performances on several real data examples but with a much-reduced computing time. This algorithm is now implemented in Monolix and in the R package Rsmlx.
Fichier principal
Vignette du fichier
Prague_Lavielle_v3_samba_main.pdf (526.14 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03410025 , version 1 (30-10-2021)

Identifiants

Citer

Mélanie Prague, Marc Lavielle. SAMBA: a Novel Method for Fast Automatic Model Building in Nonlinear Mixed-Effects Models. CPT: Pharmacometrics and Systems Pharmacology, 2022, 11 (2), ⟨10.1002/psp4.12742⟩. ⟨hal-03410025⟩
120 Consultations
224 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More