Academic Journal
OpenPMX Software for Nonlinear Mixed-Effect Models in Pharmacometrics: Precision Compared With NONMEM First-Order Conditional Estimation.
| Τίτλος: | OpenPMX Software for Nonlinear Mixed-Effect Models in Pharmacometrics: Precision Compared With NONMEM First-Order Conditional Estimation. |
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| Συγγραφείς: | Eleveld DJ; University of Groningen, University Medical Center Groningen, Department of Anesthesiology, Groningen, the Netherlands., Koomen JV; University of Groningen, University Medical Center Groningen, Department of Anesthesiology, Groningen, the Netherlands.; Department of Pharmacology, Toxicology and Kinetics, Dutch Medicines Evaluation Board, Utrecht, the Netherlands., Stevens J; University of Groningen, University Medical Center Groningen, Department of Clinical Pharmacy and Pharmacology, Groningen, the Netherlands.; University of Groningen, University Medical Center Groningen, Pharmacometric Expertise Center of the Northern Netherlands, Groningen, the Netherlands., Colin PJ; University of Groningen, University Medical Center Groningen, Department of Anesthesiology, Groningen, the Netherlands., Struys MMRF; University of Groningen, University Medical Center Groningen, Department of Anesthesiology, Groningen, the Netherlands. |
| Πηγή: | CPT: pharmacometrics & systems pharmacology [CPT Pharmacometrics Syst Pharmacol] 2026 Jun; Vol. 15 (6), pp. e70250. |
| Τύπος έκδοσης: | Journal Article; Comparative Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Wiley Country of Publication: United States NLM ID: 101580011 Publication Model: Print Cited Medium: Internet ISSN: 2163-8306 (Electronic) Linking ISSN: 21638306 NLM ISO Abbreviation: CPT Pharmacometrics Syst Pharmacol Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2015- : Hoboken, NJ : Wiley Original Publication: New York, NY : Nature Pub. Group |
| Ιατρικοί όροι (MeSH): | Software* , Nonlinear Dynamics*, Humans ; Computer Simulation |
| Περίληψη: | Mixed effects models are a backbone of pharmacometrics, and NONMEM software, with the first-order conditional method with interaction having become the de facto industry standard for model estimation. Documentation exists for the general mathematical methodology for estimation, but many technical and implementation details are lacking. OpenPMX aims to enable nonlinear mixed-effects modeling and estimation in a transparent and efficient manner, with open source licensing allowing for broad application and development. Model parameter estimation bias and root mean squared error (RMSE) obtained using OpenPMX were compared to that using NONMEM for five population models and datasets with varying degrees of complexity. For each model and dataset, repeated simulation and estimation were performed, and the per-dataset difference in precision for parameter estimates was calculated for OpenPMX versus NONMEM. We found that the bias and RMSE of OpenPMX are comparable to the industry standard NONMEM, and in some cases slightly better. The project has low complexity, few dependencies, and is open source, with all technical details open for inspection, auditing, and scientific collaboration. (© 2026 The Author(s). CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.) |
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| Contributed Indexing: | Keywords: estimation; modeling; pharmacometrics; software |
| Entry Date(s): | Date Created: 20260521 Date Completed: 20260717 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13239740 |
| DOI: | 10.1002/psp4.70250 |
| PMID: | 42166222 |
| Βάση Δεδομένων: | MEDLINE |
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