ArticleCPT: pharmacometrics & systems pharmacology2022
SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
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- Time to HIV rebound after antiretroviral therapy interruption: a double-blind randomised placebo-controlled trial of long-acting broadly neutralising antibodies; The RIO Trial.medRxiv : the preprint server for health sciences · 2026Article
- Stochastic Gates for Covariate Selection in Population Pharmacokinetics Modeling.CPT: pharmacometrics & systems pharmacology · 2026Article
- Redefining Parameter Estimation and Covariate Selection via Variational Autoencoders: One Run Is All You Need.CPT: pharmacometrics & systems pharmacology · 2025Article
- Shap-Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling.CPT: pharmacometrics & systems pharmacology · 2025Article
- Recommended approaches for integration of population pharmacokinetic modelling with precision dosing in clinical practice.British journal of clinical pharmacology · 2025Review
- Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.CPT: pharmacometrics & systems pharmacology · 2024Article
- A fully automatic tool for development of population pharmacokinetic models.CPT: pharmacometrics & systems pharmacology · 2024Article
- New pharmacodynamic parameters linked with ibrutinib responses in chronic lymphocytic leukemia: Prospective study in real-world patients and mathematical modeling.PLoS medicine · 2024Observational
- Covariate modeling in pharmacometrics: General points for consideration.CPT: pharmacometrics & systems pharmacology · 2024Review
- Evaluation of covariate effects in item response theory models.CPT: pharmacometrics & systems pharmacology · 2024Article
- Pharmacokinetic/LDL-C and exposure-response analysis of tafolecimab in Chinese hypercholesterolemia patients: Results from phase I, II, and III studies.Clinical and translational science · 2023Article
- Prediction of long-term humoral response induced by the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine against Ebola.NPJ vaccines · 2023Article
- Modeling tumor size dynamics based on real-world electronic health records and image data in advanced melanoma patients receiving immunotherapy.CPT: pharmacometrics & systems pharmacology · 2023Article
- Population pharmacokinetic modeling of daridorexant, a novel dual orexin receptor antagonist.CPT: pharmacometrics & systems pharmacology · 2023Article
- SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models.CPT: pharmacometrics & systems pharmacology · 2022Article
- Implementation of Computer-Aided Piano Music Automatic Notation Algorithm in Psychological Detoxification.Occupational therapy international · 2022Article
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Authors and funding
2 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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 Stochastic Approximation for Model Building Algorithm (SAMBA) 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 Stepwise Covariate Modeling (SCM) and COnditional Sampling use for Stepwise Approach (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.
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