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dc.contributor.authorBrown, Alexandra R.
dc.contributor.authorGajewski, Byron J.
dc.contributor.authorAaronson, Lauren
dc.contributor.authorMudaranthakam, Dinesh Pal
dc.contributor.authorHunt, Suzanne L.
dc.contributor.authorBerry, Scott M.
dc.contributor.authorQuintana, Melanie
dc.contributor.authorPasnoor, Mamatha
dc.contributor.authorDimachkie, Mazen M.
dc.contributor.authorJawdat, Omar
dc.contributor.authorHerbelin, Laura
dc.contributor.authorBarohn, Richard J.
dc.date.accessioned2017-09-11T00:17:14Z
dc.date.available2017-09-11T00:17:14Z
dc.date.issued2016-08
dc.identifier.citationBrown, A. R., Gajewski, B. J., Aaronson, L. S., Mudaranthakam, D. P., Hunt, S. L., Berry, S. M., … Barohn, R. J. (2016). A Bayesian comparative effectiveness trial in action: developing a platform for multisite study adaptive randomization. Trials, 17(1), 428. http://doi.org/10.1186/s13063-016-1544-5en_US
dc.identifier.urihttp://hdl.handle.net/1808/24939
dc.descriptionA grant from the One-University Open Access Fund at the University of Kansas was used to defray the author's publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.en_US
dc.description.abstractBackground In the last few decades, the number of trials using Bayesian methods has grown rapidly. Publications prior to 1990 included only three clinical trials that used Bayesian methods, but that number quickly jumped to 19 in the 1990s and to 99 from 2000 to 2012. While this literature provides many examples of Bayesian Adaptive Designs (BAD), none of the papers that are available walks the reader through the detailed process of conducting a BAD. This paper fills that gap by describing the BAD process used for one comparative effectiveness trial (Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations) that can be generalized for use by others. A BAD was chosen with efficiency in mind. Response-adaptive randomization allows the potential for substantially smaller sample sizes, and can provide faster conclusions about which treatment or treatments are most effective. An Internet-based electronic data capture tool, which features a randomization module, facilitated data capture across study sites and an in-house computation software program was developed to implement the response-adaptive randomization.

Results A process for adapting randomization with minimal interruption to study sites was developed. A new randomization table can be generated quickly and can be seamlessly integrated in the data capture tool with minimal interruption to study sites.

Conclusion This manuscript is the first to detail the technical process used to evaluate a multisite comparative effectiveness trial using adaptive randomization. An important opportunity for the application of Bayesian trials is in comparative effectiveness trials. The specific case study presented in this paper can be used as a model for conducting future clinical trials using a combination of statistical software and a web-based application.

Trial registration ClinicalTrials.gov Identifier: NCT02260388, registered on 6 October 2014
en_US
dc.publisherBioMed Centralen_US
dc.rights© 2016 The Author(s). Open Access This 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.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectBayesian adaptive designen_US
dc.subjectClinical trial conducten_US
dc.subjectData captureen_US
dc.subjectBayesian randomizationen_US
dc.subjectAdaptive randomizationen_US
dc.subjectResponse-adaptive randomizationen_US
dc.subjectREDCapen_US
dc.titleA Bayesian comparative effectiveness trial in action: developing a platform for multisite study adaptive randomizationen_US
dc.typeArticleen_US
kusw.kuauthorBrown, Alexandra R.
kusw.kuauthorGajewski, Byron J.
kusw.kuauthorMudaranthakam, Dinesh Pal
kusw.kuauthorHunt, Suzanne L.
kusw.kuauthorAaronson, Lauren S.
kusw.kuauthorPasnoor, Mamatha
kusw.kuauthorDimachkie, Mazen M.
kusw.kuauthorJawdat, Omar
kusw.kuauthorHerbelin, Laura
kusw.kuauthorBarohn, Richard J.
kusw.kudepartmentBiostatisticsen_US
kusw.kudepartmentNursingen_US
kusw.kudepartmentNeurologyen_US
dc.identifier.doi10.1186/s13063-016-1544-5en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.identifier.pmid5006258en_US
dc.rights.accessrightsopenAccess


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© 2016 The Author(s). Open Access This 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.
Except where otherwise noted, this item's license is described as: © 2016 The Author(s). Open Access This 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.