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Incorporating digitally derived endpoints within clinical development programs by leveraging prior work

Digital health technologies (DHTs) enable remote data collection, support a patient-centric approach to drug development, and provide real-time data in real-world settings. With increasing use of DHTs in clinical care and development, we expect a growing body of evidence supporting use of DHTs to ca...

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Autores principales: Bertha, Amy, Alaj, Rinol, Bousnina, Imein, Doyle, Megan K., Friend, Danielle, Kalamegham, Rasika, Oliva, Lauren, Knezevic, Igor, Kramer, Frank, Podhaisky, Hans-Peter, Reimann, Sven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415378/
https://www.ncbi.nlm.nih.gov/pubmed/37563201
http://dx.doi.org/10.1038/s41746-023-00886-9
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author Bertha, Amy
Alaj, Rinol
Bousnina, Imein
Doyle, Megan K.
Friend, Danielle
Kalamegham, Rasika
Oliva, Lauren
Knezevic, Igor
Kramer, Frank
Podhaisky, Hans-Peter
Reimann, Sven
author_facet Bertha, Amy
Alaj, Rinol
Bousnina, Imein
Doyle, Megan K.
Friend, Danielle
Kalamegham, Rasika
Oliva, Lauren
Knezevic, Igor
Kramer, Frank
Podhaisky, Hans-Peter
Reimann, Sven
author_sort Bertha, Amy
collection PubMed
description Digital health technologies (DHTs) enable remote data collection, support a patient-centric approach to drug development, and provide real-time data in real-world settings. With increasing use of DHTs in clinical care and development, we expect a growing body of evidence supporting use of DHTs to capture endpoint data in clinical trials. As the body of evidence grows, it will be critical to ensure that available prior work can be leveraged. We propose a framework to reuse analytical and clinical validation, as well as verification data, generated for existing DHTs. We apply real life case studies to illustrate our proposal aimed at leveraging prior work, while applying the V3 framework (verification, analytical validation, clinical validation) and avoiding duplication. Utilizing our framework will enable stakeholders to share best practices and consistent approaches to employing these tools in clinical studies, build on each other’s work, and ultimately accelerate evidence generation demonstrating the reproducibility and value add of these new tools.
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spelling pubmed-104153782023-08-12 Incorporating digitally derived endpoints within clinical development programs by leveraging prior work Bertha, Amy Alaj, Rinol Bousnina, Imein Doyle, Megan K. Friend, Danielle Kalamegham, Rasika Oliva, Lauren Knezevic, Igor Kramer, Frank Podhaisky, Hans-Peter Reimann, Sven NPJ Digit Med Comment Digital health technologies (DHTs) enable remote data collection, support a patient-centric approach to drug development, and provide real-time data in real-world settings. With increasing use of DHTs in clinical care and development, we expect a growing body of evidence supporting use of DHTs to capture endpoint data in clinical trials. As the body of evidence grows, it will be critical to ensure that available prior work can be leveraged. We propose a framework to reuse analytical and clinical validation, as well as verification data, generated for existing DHTs. We apply real life case studies to illustrate our proposal aimed at leveraging prior work, while applying the V3 framework (verification, analytical validation, clinical validation) and avoiding duplication. Utilizing our framework will enable stakeholders to share best practices and consistent approaches to employing these tools in clinical studies, build on each other’s work, and ultimately accelerate evidence generation demonstrating the reproducibility and value add of these new tools. Nature Publishing Group UK 2023-08-10 /pmc/articles/PMC10415378/ /pubmed/37563201 http://dx.doi.org/10.1038/s41746-023-00886-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Comment
Bertha, Amy
Alaj, Rinol
Bousnina, Imein
Doyle, Megan K.
Friend, Danielle
Kalamegham, Rasika
Oliva, Lauren
Knezevic, Igor
Kramer, Frank
Podhaisky, Hans-Peter
Reimann, Sven
Incorporating digitally derived endpoints within clinical development programs by leveraging prior work
title Incorporating digitally derived endpoints within clinical development programs by leveraging prior work
title_full Incorporating digitally derived endpoints within clinical development programs by leveraging prior work
title_fullStr Incorporating digitally derived endpoints within clinical development programs by leveraging prior work
title_full_unstemmed Incorporating digitally derived endpoints within clinical development programs by leveraging prior work
title_short Incorporating digitally derived endpoints within clinical development programs by leveraging prior work
title_sort incorporating digitally derived endpoints within clinical development programs by leveraging prior work
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415378/
https://www.ncbi.nlm.nih.gov/pubmed/37563201
http://dx.doi.org/10.1038/s41746-023-00886-9
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