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Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment
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.ORCID iD: 0000-0002-3712-0255
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
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2019 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 21, no 3, article id UNSP 34Article in journal (Refereed) Published
Abstract [en]

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method’s covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.

Place, publisher, year, edition, pages
2019. Vol. 21, no 3, article id UNSP 34
Keywords [en]
conditional weighted residuals, diagnostics, model evaluation, nonlinear mixed effects models, prediction bias, structural model
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-367054DOI: 10.1208/s12248-019-0305-2ISI: 000460184300001PubMedID: 30815754OAI: oai:DiVA.org:uu-367054DiVA, id: diva2:1266297
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)
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Available from: 2018-12-17 Created: 2018-11-27 Last updated: 2019-01-21

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Ibrahim, Moustafa M. A.Ueckert, SebastianKjellsson, Maria C.Karlsson, Mats O.

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