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Variability Attribution for Automated Model Building
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0002-2084-1531
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 Pharmaceutical Biosciences.ORCID iD: 0000-0003-3531-9452
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0003-1258-8297
2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 37Article in journal (Refereed) Published
Abstract [en]

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.

Place, publisher, year, edition, pages
2019. Vol. 21, no 3, article id UNSP 37
Keywords [en]
automated model building, linearization, model evaluation, nonlinear mixed effects models, stochastic model
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-367056DOI: 10.1208/s12248-019-0310-5ISI: 000460816600001PubMedID: 30850918OAI: oai:DiVA.org:uu-367056DiVA, id: diva2:1266303
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-25Bibliographically approved
In thesis
1. Pharmacometric evaluation and improvement of models and study designs - applied in diabetes
Open this publication in new window or tab >>Pharmacometric evaluation and improvement of models and study designs - applied in diabetes
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometric models are increasingly used to improve the efficiency of the drug development process and increase our understanding of the studied underlying pathophysiological system. These models require assumptions for handling different types of data and the different model components, and the appropriateness of such assumptions must be carefully inspected for unbiased conclusions. The aim of this thesis was to develop new models, that by acknowledging the complexity of the data captures more information, and novel methodologies for model evaluation, as well as applying models to improve study designs, with practical illustrations in the therapeutic area of diabetes. Two new models were developed. An integrated minimal model was developed to enable clinical trial simulations in presence of endogenous insulin secretion while deriving the important physiological indices for clinical diagnosis. A multi-state model was developed for improved handling of survival data in presence of competing risks and interval-censored data. New methodologies for model evaluations were developed that include residual modeling and linearization for assessing possible improvements of the structural and statistical model components as well as using simulations to assess the captured information from the data between structurally different models. A mapping approach for parameters carrying similar information between different models was developed, allowing the derivation of physiological indices from the integrated glucose insulin model. Models were also successfully applied with the purpose of improving study designs, either based on anticipated drug effect or for assessment of physiological indices. In conclusion, new more informative models were developed by acknowledging the complexity of the data, novel methods were proposed and applied for model development/evaluation process, and models were used to improve study designs for clinical trials and clinical diagnosis. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 75
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 264
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-366715 (URN)978-91-513-0518-9 (ISBN)
Public defence
2019-01-18, B41, BMC, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2018-12-17 Created: 2018-11-27 Last updated: 2019-01-21

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Ibrahim, Moustafa M. A.Nordgren, RikardKjellsson, Maria C.Karlsson, Mats O.

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