V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates
Abstract Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of...
| Hoofdauteurs: | , , , |
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| Formaat: | Artikel |
| Taal: | English |
| Gepubliceerd in: |
Wiley
2021-09-01
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| Reeks: | CPT: Pharmacometrics & Systems Pharmacology |
| Online toegang: | https://doi.org/10.1002/psp4.12679 |
| _version_ | 1827188566742007808 |
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| author | Jos Lommerse Nele Plock S. Y. Amy Cheung Jeffrey R. Sachs |
| author_facet | Jos Lommerse Nele Plock S. Y. Amy Cheung Jeffrey R. Sachs |
| author_sort | Jos Lommerse |
| collection | DOAJ |
| description | Abstract Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V2ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non‐ordinary‐differential‐equation‐based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V2ACHER facilitates consistent, intuitive, single‐plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross‐functional development teams and facilitating confident use in support of decisions. |
| first_indexed | 2025-03-21T07:40:07Z |
| format | Article |
| id | doaj.art-c21fd9582c8f4d6bb77d5556f2d45d1d |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2025-03-21T07:40:07Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-c21fd9582c8f4d6bb77d5556f2d45d1d2024-07-15T04:50:48ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062021-09-011091092110610.1002/psp4.12679V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariatesJos Lommerse0Nele Plock1S. Y. Amy Cheung2Jeffrey R. Sachs3Certara Strategic Consulting Princeton NJ USACertara Strategic Consulting Princeton NJ USACertara Strategic Consulting Princeton NJ USAPharmacokinetics, Pharmacodynamics, and Drug Metabolism–Quantitative Pharmacology and Pharmacometrics Research Laboratories of Merck & Co., Inc. Kenilworth NJ USAAbstract Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V2ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non‐ordinary‐differential‐equation‐based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V2ACHER facilitates consistent, intuitive, single‐plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross‐functional development teams and facilitating confident use in support of decisions.https://doi.org/10.1002/psp4.12679 |
| spellingShingle | Jos Lommerse Nele Plock S. Y. Amy Cheung Jeffrey R. Sachs V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates CPT: Pharmacometrics & Systems Pharmacology |
| title | V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates |
| title_full | V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates |
| title_fullStr | V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates |
| title_full_unstemmed | V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates |
| title_short | V2ACHER: Visualization of complex trial data in pharmacometric analyses with covariates |
| title_sort | v2acher visualization of complex trial data in pharmacometric analyses with covariates |
| url | https://doi.org/10.1002/psp4.12679 |
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