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A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)
Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and i...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069184/ https://www.ncbi.nlm.nih.gov/pubmed/32164754 http://dx.doi.org/10.1186/s13023-020-1332-x |
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author | Ghadessi, Mercedeh Tang, Rui Zhou, Joey Liu, Rong Wang, Chenkun Toyoizumi, Kiichiro Mei, Chaoqun Zhang, Lixia Deng, C. Q. Beckman, Robert A. |
author_facet | Ghadessi, Mercedeh Tang, Rui Zhou, Joey Liu, Rong Wang, Chenkun Toyoizumi, Kiichiro Mei, Chaoqun Zhang, Lixia Deng, C. Q. Beckman, Robert A. |
author_sort | Ghadessi, Mercedeh |
collection | PubMed |
description | Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs. |
format | Online Article Text |
id | pubmed-7069184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70691842020-03-18 A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) Ghadessi, Mercedeh Tang, Rui Zhou, Joey Liu, Rong Wang, Chenkun Toyoizumi, Kiichiro Mei, Chaoqun Zhang, Lixia Deng, C. Q. Beckman, Robert A. Orphanet J Rare Dis Review Historical controls (HCs) can be used for model parameter estimation at the study design phase, adaptation within a study, or supplementation or replacement of a control arm. Currently on the latter, there is no practical roadmap from design to analysis of a clinical trial to address selection and inclusion of HCs, while maintaining scientific validity. This paper provides a comprehensive roadmap for planning, conducting, analyzing and reporting of studies using HCs, mainly when a randomized clinical trial is not possible. We review recent applications of HC in clinical trials, in which either predominantly a large treatment effect overcame concerns about bias, or the trial targeted a life-threatening disease with no treatment options. In contrast, we address how the evidentiary standard of a trial can be strengthened with optimized study designs and analysis strategies, emphasizing rare and pediatric indications. We highlight the importance of simulation and sensitivity analyses for estimating the range of uncertainties in the estimation of treatment effect when traditional randomization is not possible. Overall, the paper provides a roadmap for using HCs. BioMed Central 2020-03-12 /pmc/articles/PMC7069184/ /pubmed/32164754 http://dx.doi.org/10.1186/s13023-020-1332-x Text en © The Author(s). 2020 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 | Review Ghadessi, Mercedeh Tang, Rui Zhou, Joey Liu, Rong Wang, Chenkun Toyoizumi, Kiichiro Mei, Chaoqun Zhang, Lixia Deng, C. Q. Beckman, Robert A. A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) |
title | A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) |
title_full | A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) |
title_fullStr | A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) |
title_full_unstemmed | A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) |
title_short | A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) |
title_sort | roadmap to using historical controls in clinical trials – by drug information association adaptive design scientific working group (dia-adswg) |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069184/ https://www.ncbi.nlm.nih.gov/pubmed/32164754 http://dx.doi.org/10.1186/s13023-020-1332-x |
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