Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach
| dc.rights.license | open | en_US |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | CLAIRON, Quentin | |
| dc.contributor.author | PASIN, Chloe | |
| dc.contributor.author | BALELLI, Irene | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | THIEBAUT, Rodolphe | |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | PRAGUE, Melanie | |
| dc.date.accessioned | 2023-03-06T09:58:41Z | |
| dc.date.available | 2023-03-06T09:58:41Z | |
| dc.date.issued | 2023-10-14 | |
| dc.identifier.issn | 0943-4062 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/172167 | |
| dc.description.abstractEn | We present a parameter estimation method for nonlinear mixed effect models based on ordinary differential equations (NLME-ODEs). The method presented here aims at regularizing the estimation problem in presence of model misspecifications, practical identifiability issues and unknown initial conditions. For doing so, we define our estimator as the minimizer of a cost function which incorporates a possible gap between the assumed model at the population level and the specific individual dynamic. The cost function computation leads to formulate and solve optimal control problems at the subject level. This control theory approach allows to bypass the need to know or estimate initial conditions for each subject and it regularizes the estimation problem in presence of poorly identifiable parameters. Comparing to maximum likelihood, we show on simulation examples that our method improves estimation accuracy in possibly partially observed systems with unknown initial conditions or poorly identifiable parameters with or without model error. We conclude this work with a real application on antibody concentration data after vaccination against Ebola virus coming from phase 1 trials. We use the estimated model discrepancy at the subject level to analyze the presence of model misspecification. | |
| dc.language.iso | EN | en_US |
| dc.rights | Attribution 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
| dc.subject.en | Dynamic population models | |
| dc.subject.en | Ordinary differential equations | |
| dc.subject.en | Optimal control theory | |
| dc.subject.en | Clinical trial analysis | |
| dc.title.en | Parameter estimation in nonlinear mixed effect models based on ordinary differential equations: An optimal control approach | |
| dc.title.alternative | Computation Stat | |
| dc.type | Article de revue | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.description.sponsorshipEurope | European Union’s Horizon 2020 research and innovation programme | en_US |
| bordeaux.journal | Computational Statistics | |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.team | SISTM_BPH | en_US |
| hal.export | false | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computational%20Statistics&rft.date=2023-10-14&rft.eissn=0943-4062&rft.issn=0943-4062&rft.au=CLAIRON,%20Quentin&PASIN,%20Chloe&BALELLI,%20Irene&THIEBAUT,%20Rodolphe&PRAGUE,%20Melanie&rft.genre=article |
