A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics
Abstract 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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-04-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12763 |
| _version_ | 1811329819613331456 |
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| author | Kirill Zhudenkov Sergey Gavrilov Alina Sofronova Oleg Stepanov Nataliya Kudryashova Gabriel Helmlinger Kirill Peskov |
| author_facet | Kirill Zhudenkov Sergey Gavrilov Alina Sofronova Oleg Stepanov Nataliya Kudryashova Gabriel Helmlinger Kirill Peskov |
| author_sort | Kirill Zhudenkov |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T15:50:39Z |
| format | Article |
| id | doaj.art-3586fb557e354f93bb4b2be977de2eae |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-04-13T15:50:39Z |
| publishDate | 2022-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-3586fb557e354f93bb4b2be977de2eae2022-12-22T02:40:51ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062022-04-0111442543710.1002/psp4.12763A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnosticsKirill Zhudenkov0Sergey Gavrilov1Alina Sofronova2Oleg Stepanov3Nataliya Kudryashova4Gabriel Helmlinger5Kirill Peskov6M&S Decisions LLC Moscow RussiaM&S Decisions LLC Moscow RussiaM&S Decisions LLC Moscow RussiaM&S Decisions LLC Moscow RussiaM&S Decisions LLC Moscow RussiaClinical Pharmacology & Toxicology Obsidian Therapeutics Cambridge Massachusetts USAM&S Decisions LLC Moscow RussiaAbstract 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.https://doi.org/10.1002/psp4.12763 |
| spellingShingle | Kirill Zhudenkov Sergey Gavrilov Alina Sofronova Oleg Stepanov Nataliya Kudryashova Gabriel Helmlinger Kirill Peskov A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics CPT: Pharmacometrics & Systems Pharmacology |
| 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 |
| url | https://doi.org/10.1002/psp4.12763 |
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