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  • 1.
    Bjugård Nyberg, Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Garnishing the smorgasbord of pharmacometric methods2024Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The smorgasbord of methods that we use within the field of pharmacometrics has developed steadily over several decades and is now a well-laid-out buffet. This thesis adds some garnish to the table in the form of small improvements to the handling of certain problems.

    The first problem tackled by the thesis was the challenge of saddle points and local non-identifiability when estimating pharmacometric model parameters. Substituting the common method of randomly perturbing the initial parameter estimates with one saddle-reset step enhances the accuracy of maximum likelihood estimates by overcoming saddle points parameter values, a common issue in nonlinear mixed-effects models. This algorithm, as implemented in the NONMEM software, was applied to various identifiable and nonidentifiable pharmacometric models, showing improved performance over traditional methods.

    Part of the thesis was dedicated to the development of a paediatric pharmacokinetic model for ethionamide, a drug used in treating multidrug-resistant tuberculosis. The resulting model was then used to simulate drug exposure under different dosing regimens, a new dosing regimen for children was proposed. The developed model, and therefore the proposed paediatric dosing regimen, considers factors like maturation of pharmacokinetic pathways and, administration by nasogastric tube, and concurrent rifampicin treatment. The regimen, with some modifications, was adopted in the 2022 update to the World Health Organization operational handbook on tuberculosis.

    Finally, the thesis explored novel model-integrated evidence (MIE) approaches for bioequivalence (BE) determination. Such methods could offer more robust alternatives to standard BE approached using non-compartmental analysis (NCA). Model-based methods have been shown to be advantageous in sparse data situations, such as is found in studies of ophthalmic formulations, but have suffered from inflated type I error rates. MIE BE approaches using a single model or using model averaging were presented and shown to control type I error at the nominal level while demonstrating increased power in bioequivalence determination.

    List of papers
    1. Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
    Open this publication in new window or tab >>Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
    2020 (English)In: AAPS Journal, E-ISSN 1550-7416, Vol. 22, no 4, article id 90Article in journal (Refereed) Published
    Abstract [en]

    Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.

    Keywords
    NLME, estimation methods, parameter estimation, pharmacometrics, practical identifiability
    National Category
    Pharmaceutical Sciences
    Identifiers
    urn:nbn:se:uu:diva-419009 (URN)10.1208/s12248-020-00471-y (DOI)000545830800001 ()32617704 (PubMedID)
    Available from: 2020-09-08 Created: 2020-09-08 Last updated: 2023-12-17Bibliographically approved
    2. Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis
    Open this publication in new window or tab >>Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis
    Show others...
    2020 (English)In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 64, no 3, article id e01984-19Article in journal (Refereed) Published
    Abstract [en]

    Ethionamide has proven efficacy against both drug-susceptible and some drug-resistant strains of Mycobacterium tuberculosis. Limited information on its pharmacokinetics in children is available, and current doses are extrapolated from weight-based adult doses. Pediatric doses based on more robust evidence are expected to improve antituberculosis treatment, especially in small children. In this analysis, ethionamide concentrations in children from 2 observational clinical studies conducted in Cape Town, South Africa, were pooled. All children received ethionamide once daily at a weight-based dose of approximately 20 mg/kg of body weight (range, 10.4 to 25.3 mg/kg) in combination with other first- or second-line antituberculosis medications and with antiretroviral therapy in cases of HIV coinfection. Pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling. The MDR-PK1 study contributed data for 110 children on treatment for multidrug-resistant tuberculosis, while the DATiC study contributed data for 9 children treated for drug-susceptible tuberculosis. The median age of the children in the studies combined was 2.6 years (range, 0.23 to 15 years), and the median weight was 12.5 kg (range, 2.5 to 66 kg). A one-compartment, transit absorption model with first-order elimination best described ethionamide pharmacokinetics in children. Allometric scaling of clearance (typical value, 8.88 liters/h), the volume of distribution (typical value, 21.4 liters), and maturation of clearance and absorption improved the model fit. HIV coinfection decreased the ethionamide bioavailability by 22%, rifampin coadministration increased clearance by 16%, and ethionamide administration by use of a nasogastric tube increased the rate, but the not extent, of absorption. The developed model was used to predict pediatric doses achieving the same drug exposure achieved in 50- to 70-kg adults receiving 750-mg once-daily dosing. Based on model predictions, we recommend a weight-banded pediatric dosing scheme using scored 125-mg tablets.

    Place, publisher, year, edition, pages
    American Society for Microbiology, 2020
    Keywords
    ethionamide, multidrug resistance, pediatric infectious disease, population pharmacokinetics, tuberculosis
    National Category
    Pharmaceutical Sciences
    Identifiers
    urn:nbn:se:uu:diva-407966 (URN)10.1128/AAC.01984-19 (DOI)000516763200049 ()31871093 (PubMedID)
    Funder
    The Swedish Foundation for International Cooperation in Research and Higher Education (STINT)Wellcome trust, 206379/Z/17/Z
    Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2023-12-17Bibliographically approved
    3. Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic endpoints
    Open this publication in new window or tab >>Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic endpoints
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Pharmaceutical Sciences
    Identifiers
    urn:nbn:se:uu:diva-517824 (URN)
    Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-17
    4. Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods
    Open this publication in new window or tab >>Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methods
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Pharmaceutical Sciences
    Identifiers
    urn:nbn:se:uu:diva-517823 (URN)
    Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-17
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  • 2.
    Bjugård Nyberg, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Chen, Xiaomei
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Donnelly, Mark
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Fang, Lanyan
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Zhao, Liang
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Evaluation of model-integrated evidence approaches for pharmacokinetic bioequivalence studies using model averaging methodsManuscript (preprint) (Other academic)
  • 3.
    Bjugård Nyberg, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Draper, Heather R.
    Stellenbosch Univ, Fac Med & Hlth Sci, Desmond Tutu TB Ctr, Dept Paediat & Child Hlth, Cape Town, South Africa.
    Garcia-Prats, Anthony J.
    Stellenbosch Univ, Fac Med & Hlth Sci, Desmond Tutu TB Ctr, Dept Paediat & Child Hlth, Cape Town, South Africa.
    Thee, Stephanie
    Charite, Dept Pediat, Div Pneumonol Immunol & Intens Care, Berlin, Germany.
    Bekker, Adrie
    Stellenbosch Univ, Fac Med & Hlth Sci, Desmond Tutu TB Ctr, Dept Paediat & Child Hlth, Cape Town, South Africa.
    Zar, Heather J.
    Red Cross War Mem Childrens Hosp, Dept Paediat & Child Hlth, Cape Town, South Africa;Univ Cape Town, MRC, Unit Child & Adolescent Hlth, Cape Town, South Africa.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schaaf, H. Simon
    Stellenbosch Univ, Fac Med & Hlth Sci, Desmond Tutu TB Ctr, Dept Paediat & Child Hlth, Cape Town, South Africa.
    McIlleron, Helen
    Univ Cape Town, Dept Med, Div Clin Pharmacol, Cape Town, South Africa.
    Hesseling, Anneke C.
    Stellenbosch Univ, Fac Med & Hlth Sci, Desmond Tutu TB Ctr, Dept Paediat & Child Hlth, Cape Town, South Africa.
    Denti, Paolo
    Univ Cape Town, Dept Med, Div Clin Pharmacol, Cape Town, South Africa.
    Population Pharmacokinetics and Dosing of Ethionamide in Children with Tuberculosis2020In: Antimicrobial Agents and Chemotherapy, ISSN 0066-4804, E-ISSN 1098-6596, Vol. 64, no 3, article id e01984-19Article in journal (Refereed)
    Abstract [en]

    Ethionamide has proven efficacy against both drug-susceptible and some drug-resistant strains of Mycobacterium tuberculosis. Limited information on its pharmacokinetics in children is available, and current doses are extrapolated from weight-based adult doses. Pediatric doses based on more robust evidence are expected to improve antituberculosis treatment, especially in small children. In this analysis, ethionamide concentrations in children from 2 observational clinical studies conducted in Cape Town, South Africa, were pooled. All children received ethionamide once daily at a weight-based dose of approximately 20 mg/kg of body weight (range, 10.4 to 25.3 mg/kg) in combination with other first- or second-line antituberculosis medications and with antiretroviral therapy in cases of HIV coinfection. Pharmacokinetic parameters were estimated using nonlinear mixed-effects modeling. The MDR-PK1 study contributed data for 110 children on treatment for multidrug-resistant tuberculosis, while the DATiC study contributed data for 9 children treated for drug-susceptible tuberculosis. The median age of the children in the studies combined was 2.6 years (range, 0.23 to 15 years), and the median weight was 12.5 kg (range, 2.5 to 66 kg). A one-compartment, transit absorption model with first-order elimination best described ethionamide pharmacokinetics in children. Allometric scaling of clearance (typical value, 8.88 liters/h), the volume of distribution (typical value, 21.4 liters), and maturation of clearance and absorption improved the model fit. HIV coinfection decreased the ethionamide bioavailability by 22%, rifampin coadministration increased clearance by 16%, and ethionamide administration by use of a nasogastric tube increased the rate, but the not extent, of absorption. The developed model was used to predict pediatric doses achieving the same drug exposure achieved in 50- to 70-kg adults receiving 750-mg once-daily dosing. Based on model predictions, we recommend a weight-banded pediatric dosing scheme using scored 125-mg tablets.

  • 4.
    Bjugård Nyberg, Henrik
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bauer, Robert J
    Pharmacometrics R&D, ICON CLINICAL RESEARCH LLC, Gaithersburg, Maryland, USA.
    Aoki, Yasunori
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models2020In: AAPS Journal, E-ISSN 1550-7416, Vol. 22, no 4, article id 90Article in journal (Refereed)
    Abstract [en]

    Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.

    Download full text (pdf)
    fulltext
  • 5.
    Chen, Xiaomei
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Bjugård Nyberg, Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Donnelly, Mark
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Zhao, Liang
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Fang, Lanyan
    Division of Quantitative Methods and Modelling, Office of Research and Standards, Office of Generic Drugs, Food and Drug Administration.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Hooker, Andrew
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy.
    Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic endpointsManuscript (preprint) (Other academic)
  • 6.
    Smith, Mike K.
    et al.
    Pfizer, Sandwich, Kent, England.
    Moodie, Stuart L.
    Eight Pillars Ltd, Edinburgh, Midlothian, Scotland.
    Bizzotto, Roberto
    CNR Inst Neurosci, Padua, Italy.
    Blaudez, Eric
    Lixoft, Orsay, France.
    Borella, Elisa
    Univ Pavia, Pavia, Italy.
    Carrara, Letizia
    Univ Pavia, Pavia, Italy.
    Chan, Phylinda
    Pfizer, Sandwich, Kent, England.
    Chenel, Marylore
    Servier, Paris, France.
    Comets, Emmanuelle
    INSERM, Paris, France.
    Gieschke, Ronald
    Roche, Basel, Switzerland.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Harnisch, Lutz
    Pfizer, Sandwich, Kent, England.
    Hartung, Niklas
    Free Univ Berlin, Berlin, Germany.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Karlsson, Mats O.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kaye, Richard
    Mango Solut, Chippenham, England.
    Kloft, Charlotte
    Free Univ Berlin, Berlin, Germany.
    Kokash, Natallia
    Leiden Univ, Leiden, Netherlands; UCL, London, England.
    Lavielle, Marc
    Inria, Saclay, Paris, France.
    Lestini, Giulia
    INSERM, Paris, France.
    Magni, Paolo
    Univ Pavia, Pavia, Italy.
    Mari, Andrea
    CNR Inst Neurosci, Padua, Italy.
    Mentre, France
    INSERM, Paris, France.
    Muselle, Chris
    Mango Solut, Chippenham, England.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Henrik B.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Mango Solut, Chippenham, England.
    Parra-Guillen, Zinnia P.
    Free Univ Berlin, Berlin, Germany; Univ Navarra, Navarra, Spain.
    Pasotti, Lorenzo
    Univ Pavia, Pavia, Italy.
    Rode-Kristensen, Niels
    Novo Nordisk AS, Bagsv9rd, Denmark.
    Sardu, Maria L.
    Merck Serono SA, Lausanne, Switzerland.
    Smith, Gareth R.
    Cyprotex Discovery Ltd, Sci Comp Grp, Macclesfield, Crewe, England.
    Swat, Maciej J.
    EMBL European Bioinformat Inst, Wellcome Trust Genome Campus, Hinxton, Cambs, England.
    Terranova, Nadia
    Merck Serono SA, Lausanne, Switzerland.
    Yngman, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Yvon, Florent
    EMBL European Bioinformat Inst, Wellcome Trust Genome Campus, Hinxton, Cambs, England.
    Holford, Nick H
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Univ Auckland, Auckland, New Zealand.
    Model Description Language (MDL): A Standard for Modeling and Simulation2017In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 6, no 10, p. 647-650Article in journal (Refereed)
    Abstract [en]

    Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.

  • 7. Swat, M. J.
    et al.
    Moodie, S.
    Wimalaratne, S. M.
    Kristensen, N. R.
    Lavielle, M.
    Mari, A.
    Magni, P.
    Smith, M. K.
    Bizzotto, R.
    Pasotti, L.
    Mezzalana, E.
    Comets, E.
    Sarr, C.
    Terranova, N.
    Blaudez, E.
    Chan, P.
    Chard, J.
    Chatel, K.
    Chenel, M.
    Edwards, D.
    Franklin, C.
    Giorgino, T.
    Glont, M.
    Girard, P.
    Grenon, P.
    Harling, Kajsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hooker, Andrew C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kaye, R.
    Keizer, Ron
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Kloft, C.
    Kok, J. N.
    Kokash, N.
    Laibe, C.
    Laveille, C.
    Lestini, G.
    Mentré, F.
    Munafo, A.
    Nordgren, Rikard
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Bjugård Nyberg, Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Mango Solutions, Chippenham, Wiltshire, UK.
    Parra-Guillen, Z. P.
    Plan, Elodie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Ribba, B.
    Smith, G.
    Trocóniz, I. F.
    Yvon, F.
    Milligan, P. A.
    Harnisch, L.
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Hermjakob, H.
    Le Novère, N.
    Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development2015In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 4, no 6, p. 316-319Article in journal (Refereed)
    Abstract [en]

    The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

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    fulltext
  • 8.
    Yngman, Gunnar
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Henrik B.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nyberg, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Jonsson, E. Niclas
    Pharmetheus AB, Uppsala, Sweden..
    Karlsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmacy. Pharmetheus AB, Uppsala, Sweden..
    An introduction of the full random effects model2022In: CPT: Pharmacometrics and Systems Pharmacology (PSP), E-ISSN 2163-8306, Vol. 11, no 2, p. 149-160Article in journal (Refereed)
    Abstract [en]

    The full random-effects model (FREM) is a method for determining covariate effects in mixed-effects models. Covariates are modeled as random variables, described by mean and variance. The method captures the covariate effects in estimated covariances between individual parameters and covariates. This approach is robust against issues that may cause reduced performance in methods based on estimating fixed effects (e.g., correlated covariates where the effects cannot be simultaneously identified in fixed-effects methods). FREM covariate parameterization and transformation of covariate data records can be used to alter the covariate-parameter relation. Four relations (linear, log-linear, exponential, and power) were implemented and shown to provide estimates equivalent to their fixed-effects counterparts. Comparisons between FREM and mathematically equivalent full fixed-effects models (FFEMs) were performed in original and simulated data, in the presence and absence of non-normally distributed and highly correlated covariates. These comparisons show that both FREM and FFEM perform well in the examined cases, with a slightly better estimation accuracy of parameter interindividual variability (IIV) in FREM. In addition, FREM offers the unique advantage of letting a single estimation simultaneously provide covariate effect coefficient estimates and IIV estimates for any subset of the examined covariates, including the effect of each covariate in isolation. Such subsets can be used to apply the model across data sources with different sets of available covariates, or to communicate covariate effects in a way that is not conditional on other covariates.

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    fulltext
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