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Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data

BACKGROUND: Utilizing a participation burden algorithm developed in a previous study, Tufts CSDD, in collaboration with ZS, led a workshop among 8 pharmaceutical companies to validate the methodology of benchmarking the participation burden of a set of retrospective protocols and comparing these dat...

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Autores principales: Smith, Zachary, Botto, Emily, Carney, Christopher, Bagga, Abhishek, Qutab, Bazgha, Getz, Kenneth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573794/
https://www.ncbi.nlm.nih.gov/pubmed/36245022
http://dx.doi.org/10.1007/s43441-022-00467-0
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author Smith, Zachary
Botto, Emily
Carney, Christopher
Bagga, Abhishek
Qutab, Bazgha
Getz, Kenneth
author_facet Smith, Zachary
Botto, Emily
Carney, Christopher
Bagga, Abhishek
Qutab, Bazgha
Getz, Kenneth
author_sort Smith, Zachary
collection PubMed
description BACKGROUND: Utilizing a participation burden algorithm developed in a previous study, Tufts CSDD, in collaboration with ZS, led a workshop among 8 pharmaceutical companies to validate the methodology of benchmarking the participation burden of a set of retrospective protocols and comparing these data to a prospective protocol design. METHODS: Eight participating companies collected data for 66 retrospective protocols and participation burden scores were calculated for each. Data from one prospective protocol was provided and prospective burden scores were compared to mean retrospective protocol burden for each company. Participating companies provided feedback on data collection process and final reports. RESULTS: Comparisons between retrospective and prospective burden scores revealed higher comparative burden in lab and blood procedures. Companies were able to gather most requested data, but some variables hypothesized to affect burden were not available to sponsors. Time constraints were reported as a challenge throughout the data collection process. CONCLUSIONS: Feedback indicated the need for establishing a larger database to enable comparisons between protocols with the same therapeutic area and indication. Investigating the impact of standard of care burden by indication on overall participation burden and encouraging sponsors to collect more accurate data contributing to participation burden at the site level are also important takeaways from this exercise.
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spelling pubmed-95737942022-10-17 Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data Smith, Zachary Botto, Emily Carney, Christopher Bagga, Abhishek Qutab, Bazgha Getz, Kenneth Ther Innov Regul Sci Original Research BACKGROUND: Utilizing a participation burden algorithm developed in a previous study, Tufts CSDD, in collaboration with ZS, led a workshop among 8 pharmaceutical companies to validate the methodology of benchmarking the participation burden of a set of retrospective protocols and comparing these data to a prospective protocol design. METHODS: Eight participating companies collected data for 66 retrospective protocols and participation burden scores were calculated for each. Data from one prospective protocol was provided and prospective burden scores were compared to mean retrospective protocol burden for each company. Participating companies provided feedback on data collection process and final reports. RESULTS: Comparisons between retrospective and prospective burden scores revealed higher comparative burden in lab and blood procedures. Companies were able to gather most requested data, but some variables hypothesized to affect burden were not available to sponsors. Time constraints were reported as a challenge throughout the data collection process. CONCLUSIONS: Feedback indicated the need for establishing a larger database to enable comparisons between protocols with the same therapeutic area and indication. Investigating the impact of standard of care burden by indication on overall participation burden and encouraging sponsors to collect more accurate data contributing to participation burden at the site level are also important takeaways from this exercise. Springer International Publishing 2022-10-16 2023 /pmc/articles/PMC9573794/ /pubmed/36245022 http://dx.doi.org/10.1007/s43441-022-00467-0 Text en © The Author(s), under exclusive licence to The Drug Information Association, Inc 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Smith, Zachary
Botto, Emily
Carney, Christopher
Bagga, Abhishek
Qutab, Bazgha
Getz, Kenneth
Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data
title Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data
title_full Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data
title_fullStr Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data
title_full_unstemmed Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data
title_short Insights from a Multi-company Workshop to Apply a Patient Participation Burden Algorithm to Protocol Data
title_sort insights from a multi-company workshop to apply a patient participation burden algorithm to protocol data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573794/
https://www.ncbi.nlm.nih.gov/pubmed/36245022
http://dx.doi.org/10.1007/s43441-022-00467-0
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