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SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models.

Mélanie Prague1,2, Marc Lavielle3

  • 1Inria Bordeaux Sud-Ouest, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, University of Bordeaux, Bordeaux, France.

CPT: Pharmacometrics & Systems Pharmacology
|February 1, 2022
PubMed
Summary
This summary is machine-generated.

Identifying components of nonlinear mixed-effects models is complex. The Stochastic Approximation for Model Building Algorithm (SAMBA) speeds up this process, offering similar performance to other methods with reduced computation time.

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Area of Science:

  • Pharmacometrics
  • Statistical modeling
  • Computational biology

Background:

  • Nonlinear mixed-effects (NLME) model building is challenging, involving structural model identification, covariate relationships, random effects correlations, and residual error modeling.
  • Current methods for NLME model identification can be computationally intensive and complex.

Purpose of the Study:

  • To introduce the Stochastic Approximation for Model Building Algorithm (SAMBA) for efficient NLME model identification.
  • To demonstrate SAMBA's ability to improve model components iteratively, even with initial suboptimal models.

Main Methods:

  • The study presents the SAMBA procedure, an iterative algorithm designed to guide the model-building process in NLME.
  • SAMBA operates by "learning" about the optimal model structure through successive approximations.

Main Results:

  • SAMBA demonstrates comparable performance to established methods like Stepwise Covariate Modeling (SCM) and COnditional Sampling use for Stepwise Approach (COSSAC) on real datasets.
  • A key finding is the significantly reduced computing time associated with the SAMBA procedure.

Conclusions:

  • SAMBA offers an efficient and effective approach to building complex nonlinear mixed-effects models.
  • The algorithm's implementation in Monolix and the R package Rsmlx facilitates its application in practical research.