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A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics
Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early‐phase studies and patient‐reported outcomes as well as event risks or rates in late‐phase studies. In recent years, a systematic trend in clinical trial data analytic...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007602/ https://www.ncbi.nlm.nih.gov/pubmed/35064957 http://dx.doi.org/10.1002/psp4.12763 |
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author | Zhudenkov, Kirill Gavrilov, Sergey Sofronova, Alina Stepanov, Oleg Kudryashova, Nataliya Helmlinger, Gabriel Peskov, Kirill |
author_facet | Zhudenkov, Kirill Gavrilov, Sergey Sofronova, Alina Stepanov, Oleg Kudryashova, Nataliya Helmlinger, Gabriel Peskov, Kirill |
author_sort | Zhudenkov, Kirill |
collection | PubMed |
description | Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early‐phase studies and patient‐reported outcomes as well as event risks or rates in late‐phase studies. In recent years, a systematic trend in clinical trial data analytics and modeling has been observed, where retrospective data are integrated into a quantitative framework to prospectively support analyses of interim data and design of ongoing and future studies of novel therapeutics. Joint modeling is an advanced statistical methodology that allows for the investigation of clinical trial outcomes by quantifying the association between baseline and/or longitudinal biomarkers and event risk. Using an exemplar data set from non‐small cell lung cancer studies, we propose and test a workflow for joint modeling. It allows a modeling scientist to comprehensively explore the data, build survival models, investigate goodness‐of‐fit, and subsequently perform outcome predictions using interim biomarker data from an ongoing study. The workflow illustrates a full process, from data exploration to predictive simulations, for selected multivariate linear and nonlinear mixed‐effects models and software tools in an integrative and exhaustive manner. |
format | Online Article Text |
id | pubmed-9007602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90076022022-04-15 A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics Zhudenkov, Kirill Gavrilov, Sergey Sofronova, Alina Stepanov, Oleg Kudryashova, Nataliya Helmlinger, Gabriel Peskov, Kirill CPT Pharmacometrics Syst Pharmacol Tutorials Clinical trials investigate treatment endpoints that usually include measurements of pharmacodynamic and efficacy biomarkers in early‐phase studies and patient‐reported outcomes as well as event risks or rates in late‐phase studies. In recent years, a systematic trend in clinical trial data analytics and modeling has been observed, where retrospective data are integrated into a quantitative framework to prospectively support analyses of interim data and design of ongoing and future studies of novel therapeutics. Joint modeling is an advanced statistical methodology that allows for the investigation of clinical trial outcomes by quantifying the association between baseline and/or longitudinal biomarkers and event risk. Using an exemplar data set from non‐small cell lung cancer studies, we propose and test a workflow for joint modeling. It allows a modeling scientist to comprehensively explore the data, build survival models, investigate goodness‐of‐fit, and subsequently perform outcome predictions using interim biomarker data from an ongoing study. The workflow illustrates a full process, from data exploration to predictive simulations, for selected multivariate linear and nonlinear mixed‐effects models and software tools in an integrative and exhaustive manner. John Wiley and Sons Inc. 2022-02-21 2022-04 /pmc/articles/PMC9007602/ /pubmed/35064957 http://dx.doi.org/10.1002/psp4.12763 Text en © 2022 M&S Decisions LLC. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Tutorials Zhudenkov, Kirill Gavrilov, Sergey Sofronova, Alina Stepanov, Oleg Kudryashova, Nataliya Helmlinger, Gabriel Peskov, Kirill A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics |
title | A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics |
title_full | A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics |
title_fullStr | A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics |
title_full_unstemmed | A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics |
title_short | A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics |
title_sort | workflow for the joint modeling of longitudinal and event data in the development of therapeutics: tools, statistical methods, and diagnostics |
topic | Tutorials |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007602/ https://www.ncbi.nlm.nih.gov/pubmed/35064957 http://dx.doi.org/10.1002/psp4.12763 |
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