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A Stability-Enhanced Lasso Approach for Covariate Selection in Non-Linear Mixed Effects Model.

Auriane Gabaut1,2,3, Rodolphe Thiébaut1,2,3,4, Cécile Proust-Lima1

  • 1University of Bordeaux, INSERM, BPH, Bordeaux, France.

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|January 22, 2026
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Summary
This summary is machine-generated.

This study introduces Lasso-SAMBA, a novel method for selecting important variables in complex biological models. It improves accuracy and reduces errors in high-dimensional data, enhancing systems vaccinology research.

Keywords:
Lasso regressionhigh‐dimensionmechanistic modelsmodel selectionnon‐linear mixed effects model

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Pharmacometrics

Background:

  • Non-linear mixed effects models (NLMEMs) using ordinary differential equations (ODEs) are crucial for analyzing dynamic biological systems.
  • Identifying relevant covariates in high-dimensional data for these models is a significant challenge.

Purpose of the Study:

  • To develop a robust method for covariate selection in ODE-based NLMEMs.
  • To improve model building in complex biological systems, particularly in high-dimensional settings.

Main Methods:

  • Introduction of Lasso-SAMBA (Stochastic Approximation for Model Building Algorithm), integrating Lasso regression with stability selection.
  • Iterative model construction coupling penalized regression with mechanistic model estimation via the SAEM algorithm.
  • Extension of the previous SAMBA strategy with a penalized, stability-driven approach to enhance robustness and reduce false discoveries.

Main Results:

  • Lasso-SAMBA demonstrated superior variable selection fidelity, False Discovery Proportion (FDR) control, and computational efficiency in simulations.
  • The method outperformed conventional stepwise and Bayesian variable selection techniques.
  • Application to a Varicella-Zoster virus vaccination study revealed robust associations between immune response model parameters and early transcriptomic expressions.

Conclusions:

  • Lasso-SAMBA offers a powerful and robust approach for high-dimensional covariate selection in ODE-based NLMEMs.
  • The method has practical utility in systems vaccinology and other fields involving complex biological modeling.
  • The R package implementation facilitates the application of Lasso-SAMBA in research.