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However, selecting relevant covariates associated with dynamics in high\u2010dimensional settings remains a major challenge. This study introduces a novel model\u2010building approach called Lasso\u2010SAMBA (SAMBA: Stochastic Approximation for Model Building Algorithm) that integrates Lasso regression with a stability selection algorithm for robust covariate selection within ODE\u2010based NLMEMs. The method iteratively constructs models by coupling penalized regression with mechanistic model estimation using the SAEM algorithm. It extends a prior strategy named SAMBA, originally based on stepwise inclusion, by replacing this step with a penalized, stability\u2010driven approach that reduces false discoveries and improves selection robustness. By maintaining the monotonic decrease of the information criterion through a calibrated exploration of penalization parameters, the proposed method outperforms conventional stepwise and Bayesian variable selection alternatives. Extensive simulation studies, spanning pharmacokinetic and immunological models, demonstrate the superiority of Lasso\u2010SAMBA in variable selection fidelity, FDR (False Discovery Proportion) control, and computational efficiency. The Lasso\u2010SAMBA method is implemented in an R package. Applied to a Varicella\u2010Zoster virus vaccination study, the method reveals robust, biologically plausible associations between parameters of the mechanistic model of the humoral immune response and early transcriptomic expressions. These results underscore the practical utility of our method for high\u2010dimensional model building in systems vaccinology and beyond.<\/jats:p>","DOI":"10.1002\/sim.70407","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:45:52Z","timestamp":1769100352000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Stability\u2010Enhanced Lasso Approach for Covariate Selection in Non\u2010Linear Mixed Effects Model"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5669-6876","authenticated-orcid":false,"given":"Auriane","family":"Gabaut","sequence":"first","affiliation":[{"name":"University of Bordeaux, INSERM, BPH  Bordeaux France"},{"name":"University of Bordeaux  Bordeaux France"},{"name":"Facult\u00e9 de m\u00e9decine Universit\u00e9 Paris\u2010Est Cr\u00e9teil, Vaccine Reasearch Institute  Cr\u00e9teil France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodolphe","family":"Thi\u00e9baut","sequence":"additional","affiliation":[{"name":"University of Bordeaux, INSERM, BPH  Bordeaux France"},{"name":"University of Bordeaux  Bordeaux France"},{"name":"Facult\u00e9 de m\u00e9decine Universit\u00e9 Paris\u2010Est Cr\u00e9teil, Vaccine Reasearch Institute  Cr\u00e9teil France"},{"name":"CHU de Bordeaux, Service d'Information M\u00e9dicale, INSERM  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9cile","family":"Proust\u2010Lima","sequence":"additional","affiliation":[{"name":"University of Bordeaux, INSERM, BPH  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e9lanie","family":"Prague","sequence":"additional","affiliation":[{"name":"University of Bordeaux, INSERM, BPH  Bordeaux France"},{"name":"University of Bordeaux  Bordeaux France"},{"name":"Facult\u00e9 de m\u00e9decine Universit\u00e9 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