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Development of visual predictive checks accounting for multimodal parameter distributions in mixture models

The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describin...

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Autores principales: Arshad, Usman, Chasseloup, Estelle, Nordgren, Rikard, Karlsson, Mats O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560505/
https://www.ncbi.nlm.nih.gov/pubmed/30968312
http://dx.doi.org/10.1007/s10928-019-09632-9
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author Arshad, Usman
Chasseloup, Estelle
Nordgren, Rikard
Karlsson, Mats O.
author_facet Arshad, Usman
Chasseloup, Estelle
Nordgren, Rikard
Karlsson, Mats O.
author_sort Arshad, Usman
collection PubMed
description The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IP(mix)) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IP(mix) assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10928-019-09632-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-65605052019-06-26 Development of visual predictive checks accounting for multimodal parameter distributions in mixture models Arshad, Usman Chasseloup, Estelle Nordgren, Rikard Karlsson, Mats O. J Pharmacokinet Pharmacodyn Original Paper The assumption of interindividual variability being unimodally distributed in nonlinear mixed effects models does not hold when the population under study displays multimodal parameter distributions. Mixture models allow the identification of parameters characteristic to a subpopulation by describing these multimodalities. Visual predictive check (VPC) is a standard simulation based diagnostic tool, but not yet adapted to account for multimodal parameter distributions. Mixture model analysis provides the probability for an individual to belong to a subpopulation (IP(mix)) and the most likely subpopulation for an individual to belong to (MIXEST). Using simulated data examples, two implementation strategies were followed to split the data into subpopulations for the development of mixture model specific VPCs. The first strategy splits the observed and simulated data according to the MIXEST assignment. A shortcoming of the MIXEST-based allocation strategy was a biased allocation towards the dominating subpopulation. This shortcoming was avoided by splitting observed and simulated data according to the IP(mix) assignment. For illustration purpose, the approaches were also applied to an irinotecan mixture model demonstrating 36% lower clearance of irinotecan metabolite (SN-38) in individuals with UGT1A1 homo/heterozygote versus wild-type genotype. VPCs with segregated subpopulations were helpful in identifying model misspecifications which were not evident with standard VPCs. The new tool provides an enhanced power of evaluation of mixture models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10928-019-09632-9) contains supplementary material, which is available to authorized users. Springer US 2019-04-09 2019 /pmc/articles/PMC6560505/ /pubmed/30968312 http://dx.doi.org/10.1007/s10928-019-09632-9 Text en © The Author(s) 2019 Open AccessThis 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 Original Paper
Arshad, Usman
Chasseloup, Estelle
Nordgren, Rikard
Karlsson, Mats O.
Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
title Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
title_full Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
title_fullStr Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
title_full_unstemmed Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
title_short Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
title_sort development of visual predictive checks accounting for multimodal parameter distributions in mixture models
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6560505/
https://www.ncbi.nlm.nih.gov/pubmed/30968312
http://dx.doi.org/10.1007/s10928-019-09632-9
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