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Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic
BACKGROUND: Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, impleme...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8771176/ https://www.ncbi.nlm.nih.gov/pubmed/35057758 http://dx.doi.org/10.1186/s12874-022-01512-0 |
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author | Ewings, Sean Saunders, Geoff Jaki, Thomas Mozgunov, Pavel |
author_facet | Ewings, Sean Saunders, Geoff Jaki, Thomas Mozgunov, Pavel |
author_sort | Ewings, Sean |
collection | PubMed |
description | BACKGROUND: Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge. METHODS: We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments. RESULTS: We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs. CONCLUSIONS: This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01512-0. |
format | Online Article Text |
id | pubmed-8771176 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87711762022-01-20 Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic Ewings, Sean Saunders, Geoff Jaki, Thomas Mozgunov, Pavel BMC Med Res Methodol Research BACKGROUND: Modern designs for dose-finding studies (e.g., model-based designs such as continual reassessment method) have been shown to substantially improve the ability to determine a suitable dose for efficacy testing when compared to traditional designs such as the 3 + 3 design. However, implementing such designs requires time and specialist knowledge. METHODS: We present a practical approach to developing a model-based design to help support uptake of these methods; in particular, we lay out how to derive the necessary parameters and who should input, and when, to these decisions. Designing a model-based, dose-finding trial is demonstrated using a treatment within the AGILE platform trial, a phase I/II adaptive design for novel COVID-19 treatments. RESULTS: We present discussion of the practical delivery of AGILE, covering what information was found to support principled decision making by the Safety Review Committee, and what could be contained within a statistical analysis plan. We also discuss additional challenges we encountered in the study and discuss more generally what (unplanned) adaptations may be acceptable (or not) in studies using model-based designs. CONCLUSIONS: This example demonstrates both how to design and deliver an adaptive dose-finding trial in order to support uptake of these methods. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01512-0. BioMed Central 2022-01-20 /pmc/articles/PMC8771176/ /pubmed/35057758 http://dx.doi.org/10.1186/s12874-022-01512-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ewings, Sean Saunders, Geoff Jaki, Thomas Mozgunov, Pavel Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic |
title | Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic |
title_full | Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic |
title_fullStr | Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic |
title_full_unstemmed | Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic |
title_short | Practical recommendations for implementing a Bayesian adaptive phase I design during a pandemic |
title_sort | practical recommendations for implementing a bayesian adaptive phase i design during a pandemic |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8771176/ https://www.ncbi.nlm.nih.gov/pubmed/35057758 http://dx.doi.org/10.1186/s12874-022-01512-0 |
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