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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
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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. |
format | Online Article Text |
id | pubmed-6505507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
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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