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A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making
In clinical trials, when exploring multiple dose groups to establish efficacy and safety on one or more selected doses, adaptive designs with interim dose selection are often used for dropping less effective dose groups. When it takes a long time to observe primary outcomes, utilizing information on...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205753/ https://www.ncbi.nlm.nih.gov/pubmed/32395663 http://dx.doi.org/10.1016/j.conctc.2020.100562 |
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author | Chen, Xiaotian Hartford, Alan Zhao, Jun |
author_facet | Chen, Xiaotian Hartford, Alan Zhao, Jun |
author_sort | Chen, Xiaotian |
collection | PubMed |
description | In clinical trials, when exploring multiple dose groups to establish efficacy and safety on one or more selected doses, adaptive designs with interim dose selection are often used for dropping less effective dose groups. When it takes a long time to observe primary outcomes, utilizing information on a surrogate endpoint available at an earlier interim may be preferred for selecting which dose to continue. We propose a Bayesian model-based approach where historical data can be leveraged to incorporate a correlation model for investigating the design's operating characteristics. Simulation studies were conducted and the method can be readily applied for power and sample size calculations. |
format | Online Article Text |
id | pubmed-7205753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72057532020-05-11 A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making Chen, Xiaotian Hartford, Alan Zhao, Jun Contemp Clin Trials Commun Article In clinical trials, when exploring multiple dose groups to establish efficacy and safety on one or more selected doses, adaptive designs with interim dose selection are often used for dropping less effective dose groups. When it takes a long time to observe primary outcomes, utilizing information on a surrogate endpoint available at an earlier interim may be preferred for selecting which dose to continue. We propose a Bayesian model-based approach where historical data can be leveraged to incorporate a correlation model for investigating the design's operating characteristics. Simulation studies were conducted and the method can be readily applied for power and sample size calculations. Elsevier 2020-04-19 /pmc/articles/PMC7205753/ /pubmed/32395663 http://dx.doi.org/10.1016/j.conctc.2020.100562 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Xiaotian Hartford, Alan Zhao, Jun A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
title | A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
title_full | A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
title_fullStr | A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
title_full_unstemmed | A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
title_short | A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
title_sort | model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7205753/ https://www.ncbi.nlm.nih.gov/pubmed/32395663 http://dx.doi.org/10.1016/j.conctc.2020.100562 |
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