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Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN
BACKGROUND: Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving partic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641102/ https://www.ncbi.nlm.nih.gov/pubmed/37965484 http://dx.doi.org/10.1016/j.conctc.2023.101220 |
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author | Brown, Alexandra R. Gajewski, Byron J. Mudaranthakam, Dinesh Pal Pasnoor, Mamatha Dimachkie, Mazen M. Jawdat, Omar Herbelin, Laura Mayo, Matthew S. Barohn, Richard J. |
author_facet | Brown, Alexandra R. Gajewski, Byron J. Mudaranthakam, Dinesh Pal Pasnoor, Mamatha Dimachkie, Mazen M. Jawdat, Omar Herbelin, Laura Mayo, Matthew S. Barohn, Richard J. |
author_sort | Brown, Alexandra R. |
collection | PubMed |
description | BACKGROUND: Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information. METHODS: PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments. RESULTS: Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin. CONCLUSIONS: Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials. |
format | Online Article Text |
id | pubmed-10641102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-106411022023-11-14 Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN Brown, Alexandra R. Gajewski, Byron J. Mudaranthakam, Dinesh Pal Pasnoor, Mamatha Dimachkie, Mazen M. Jawdat, Omar Herbelin, Laura Mayo, Matthew S. Barohn, Richard J. Contemp Clin Trials Commun Article BACKGROUND: Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information. METHODS: PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments. RESULTS: Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin. CONCLUSIONS: Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials. Elsevier 2023-10-14 /pmc/articles/PMC10641102/ /pubmed/37965484 http://dx.doi.org/10.1016/j.conctc.2023.101220 Text en © 2023 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Brown, Alexandra R. Gajewski, Byron J. Mudaranthakam, Dinesh Pal Pasnoor, Mamatha Dimachkie, Mazen M. Jawdat, Omar Herbelin, Laura Mayo, Matthew S. Barohn, Richard J. Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN |
title | Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN |
title_full | Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN |
title_fullStr | Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN |
title_full_unstemmed | Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN |
title_short | Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN |
title_sort | conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for cspn |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10641102/ https://www.ncbi.nlm.nih.gov/pubmed/37965484 http://dx.doi.org/10.1016/j.conctc.2023.101220 |
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