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Model-Based Residual Post-Processing for Residual Model Identification
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Helwan Univ, Dept Pharm Practice, Cairo, Egypt.
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
2018 (English)In: AAPS Journal, ISSN 1550-7416, E-ISSN 1550-7416, Vol. 20, no 5, article id 81Article in journal (Refereed) Published
Abstract [en]

The purpose of this study was to investigate if model-based post-processing of common diagnostics can be used as a diagnostic tool to quantitatively identify model misspecifications and rectifying actions. The main investigated diagnostic is conditional weighted residuals (CWRES). We have selected to showcase this principle with residual unexplained variability (RUV) models, where the new diagnostic tool is used to scan extended RUV models and assess in a fast and robust way whether, and what, extensions are expected to provide a superior description of data. The extended RUV models evaluated were autocorrelated errors, dynamic transform both sides, inter-individual variability on RUV, power error model, t-distributed errors, and time-varying error magnitude. The agreement in improvement in goodness-of-fit between implementing these extended RUV models on the original model and implementing these extended RUV models on CWRES was evaluated in real and simulated data examples. Real data exercise was applied to three other diagnostics: conditional weighted residuals with interaction (CWRESI), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE). CWRES modeling typically predicted (i) the nature of model misspecifications, (ii) the magnitude of the expected improvement in fit in terms of difference in objective function value (Delta OFV), and (iii) the parameter estimates associated with the model extension. Alternative metrics (CWRESI, IWRES, and NPDE) also provided valuable information, but with a lower predictive performance of Delta OFV compared to CWRES. This method is a fast and easily automated diagnostic tool for RUV model development/evaluation process; it is already implemented in the software package PsN.

Place, publisher, year, edition, pages
SPRINGER , 2018. Vol. 20, no 5, article id 81
Keywords [en]
conditional weighted residuals, diagnostics, model evaluation, nonlinear mixed effects models, residual error model
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-360174DOI: 10.1208/s12248-018-0240-7ISI: 000437188800001PubMedID: 29968184OAI: oai:DiVA.org:uu-360174DiVA, id: diva2:1247988
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-11-27Bibliographically 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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Nordgren, RikardKjellsson, Maria C.Karlsson, Mats O

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