Development of visual predictive checks accounting for multimodal parameter distributions in mixture models
| Autor: | Mats O. Karlsson, Rikard Nordgren, Usman Arshad, Estelle Chasseloup |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Population Pharmacology toxicology Irinotecan 030226 pharmacology & pharmacy Models Biological 03 medical and health sciences 0302 clinical medicine Humans Pharmacokinetics Computer Simulation Glucuronosyltransferase education Simulation based Probability Pharmacology education.field_of_study Original Paper business.industry Pattern recognition Multimodal parameter distributions Mixture model Visual predictive checks Nonlinear system Identification (information) Pharmacodynamics Nonlinear Dynamics 030220 oncology & carcinogenesis Simulated data Mixed effects Artificial intelligence business Mixture models |
| Zdroj: | Journal of Pharmacokinetics and Pharmacodynamics |
| ISSN: | 1573-8744 1567-567X |
| Popis: | 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 (IPmix) 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 IPmix 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. |
| Databáze: | OpenAIRE |
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