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Using Bayesian adaptive designs to improve phase III trials: a respiratory care example

BACKGROUND: Bayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. The aim of this work was to determine how Bayesian adaptive designs can be constructed for...

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Autores principales: Ryan, Elizabeth G., Bruce, Julie, Metcalfe, Andrew J., Stallard, Nigel, Lamb, Sarah E., Viele, Kert, Young, Duncan, Gates, Simon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515675/
https://www.ncbi.nlm.nih.gov/pubmed/31088354
http://dx.doi.org/10.1186/s12874-019-0739-3
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author Ryan, Elizabeth G.
Bruce, Julie
Metcalfe, Andrew J.
Stallard, Nigel
Lamb, Sarah E.
Viele, Kert
Young, Duncan
Gates, Simon
author_facet Ryan, Elizabeth G.
Bruce, Julie
Metcalfe, Andrew J.
Stallard, Nigel
Lamb, Sarah E.
Viele, Kert
Young, Duncan
Gates, Simon
author_sort Ryan, Elizabeth G.
collection PubMed
description BACKGROUND: Bayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. The aim of this work was to determine how Bayesian adaptive designs can be constructed for phase III clinical trials in critical care, and to assess the influence that Bayesian designs would have on trial efficiency and study results. METHODS: We re-designed the High Frequency OSCillation in Acute Respiratory distress syndrome (OSCAR) trial using Bayesian adaptive design methods, to allow for the possibility of early stopping for success or futility. We constructed several alternative designs and studied their operating characteristics via simulation. We then performed virtual re-executions by applying the Bayesian adaptive designs using the OSCAR data to demonstrate the practical applicability of the designs. RESULTS: We constructed five alternative Bayesian adaptive designs and identified a preferred design based on the simulated operating characteristics, which had similar power to the original design but recruited fewer patients on average. The virtual re-executions showed the Bayesian sequential approach and original OSCAR trial yielded similar trial conclusions. However, using a Bayesian sequential design could have led to a reduced sample size and earlier completion of the trial. CONCLUSIONS: Using the OSCAR trial as an example, this case study found that Bayesian adaptive designs can be constructed for phase III critical care trials. If the OSCAR trial had been run using one of the proposed Bayesian adaptive designs, it would have terminated at a smaller sample size with fewer deaths in the trial, whilst reaching the same conclusions. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials. TRIAL REGISTRATION: OSCAR Trial registration ISRCTN, ISRCTN10416500. Retrospectively registered 13 June 2007. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0739-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-65156752019-05-21 Using Bayesian adaptive designs to improve phase III trials: a respiratory care example Ryan, Elizabeth G. Bruce, Julie Metcalfe, Andrew J. Stallard, Nigel Lamb, Sarah E. Viele, Kert Young, Duncan Gates, Simon BMC Med Res Methodol Research Article BACKGROUND: Bayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. The aim of this work was to determine how Bayesian adaptive designs can be constructed for phase III clinical trials in critical care, and to assess the influence that Bayesian designs would have on trial efficiency and study results. METHODS: We re-designed the High Frequency OSCillation in Acute Respiratory distress syndrome (OSCAR) trial using Bayesian adaptive design methods, to allow for the possibility of early stopping for success or futility. We constructed several alternative designs and studied their operating characteristics via simulation. We then performed virtual re-executions by applying the Bayesian adaptive designs using the OSCAR data to demonstrate the practical applicability of the designs. RESULTS: We constructed five alternative Bayesian adaptive designs and identified a preferred design based on the simulated operating characteristics, which had similar power to the original design but recruited fewer patients on average. The virtual re-executions showed the Bayesian sequential approach and original OSCAR trial yielded similar trial conclusions. However, using a Bayesian sequential design could have led to a reduced sample size and earlier completion of the trial. CONCLUSIONS: Using the OSCAR trial as an example, this case study found that Bayesian adaptive designs can be constructed for phase III critical care trials. If the OSCAR trial had been run using one of the proposed Bayesian adaptive designs, it would have terminated at a smaller sample size with fewer deaths in the trial, whilst reaching the same conclusions. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials. TRIAL REGISTRATION: OSCAR Trial registration ISRCTN, ISRCTN10416500. Retrospectively registered 13 June 2007. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0739-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-14 /pmc/articles/PMC6515675/ /pubmed/31088354 http://dx.doi.org/10.1186/s12874-019-0739-3 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ryan, Elizabeth G.
Bruce, Julie
Metcalfe, Andrew J.
Stallard, Nigel
Lamb, Sarah E.
Viele, Kert
Young, Duncan
Gates, Simon
Using Bayesian adaptive designs to improve phase III trials: a respiratory care example
title Using Bayesian adaptive designs to improve phase III trials: a respiratory care example
title_full Using Bayesian adaptive designs to improve phase III trials: a respiratory care example
title_fullStr Using Bayesian adaptive designs to improve phase III trials: a respiratory care example
title_full_unstemmed Using Bayesian adaptive designs to improve phase III trials: a respiratory care example
title_short Using Bayesian adaptive designs to improve phase III trials: a respiratory care example
title_sort using bayesian adaptive designs to improve phase iii trials: a respiratory care example
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515675/
https://www.ncbi.nlm.nih.gov/pubmed/31088354
http://dx.doi.org/10.1186/s12874-019-0739-3
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