Variability Attribution for Automated Model Building

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 t...

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Autores principales: Ibrahim, Moustafa M. A., Nordgren, Rikard, Kjellsson, Maria C., Karlsson, Mats O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505507/
https://www.ncbi.nlm.nih.gov/pubmed/30850918
http://dx.doi.org/10.1208/s12248-019-0310-5
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author Ibrahim, Moustafa M. A.
Nordgren, Rikard
Kjellsson, Maria C.
Karlsson, Mats O.
author_facet Ibrahim, Moustafa M. A.
Nordgren, Rikard
Kjellsson, Maria C.
Karlsson, Mats O.
author_sort Ibrahim, Moustafa M. A.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1208/s12248-019-0310-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-65055072019-05-28 Variability Attribution for Automated Model Building Ibrahim, Moustafa M. A. Nordgren, Rikard Kjellsson, Maria C. Karlsson, Mats O. AAPS J Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1208/s12248-019-0310-5) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-03-08 /pmc/articles/PMC6505507/ /pubmed/30850918 http://dx.doi.org/10.1208/s12248-019-0310-5 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research Article
Ibrahim, Moustafa M. A.
Nordgren, Rikard
Kjellsson, Maria C.
Karlsson, Mats O.
Variability Attribution for Automated Model Building
title Variability Attribution for Automated Model Building
title_full Variability Attribution for Automated Model Building
title_fullStr Variability Attribution for Automated Model Building
title_full_unstemmed Variability Attribution for Automated Model Building
title_short Variability Attribution for Automated Model Building
title_sort variability attribution for automated model building
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505507/
https://www.ncbi.nlm.nih.gov/pubmed/30850918
http://dx.doi.org/10.1208/s12248-019-0310-5
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