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The inclusion of real world evidence in clinical development planning
BACKGROUND: When designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6116448/ https://www.ncbi.nlm.nih.gov/pubmed/30157904 http://dx.doi.org/10.1186/s13063-018-2769-2 |
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author | Martina, Reynaldo Jenkins, David Bujkiewicz, Sylwia Dequen, Pascale Abrams, Keith |
author_facet | Martina, Reynaldo Jenkins, David Bujkiewicz, Sylwia Dequen, Pascale Abrams, Keith |
author_sort | Martina, Reynaldo |
collection | PubMed |
description | BACKGROUND: When designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects are typically obtained from randomised controlled clinical trials (RCTs). Based on the observed estimates of treatment effect, variability and the minimum clinical relevant difference to detect, the sample size for a subsequent trial is estimated. However, data from real world evidence (RWE) studies, such as observational studies and other interventional studies in patients in routine clinical practice, are not widely used in a systematic manner when designing studies. In this paper, we propose a framework for inclusion of RWE in planning of a clinical development programme. METHODS: In our proposed approach, all evidence, from both RCTs and RWE (i.e. from studies in routine clinical practice), available at the time of designing of a new clinical trial is combined in a Bayesian network meta-analysis (NMA). The results can be used to inform the design of the next clinical trial in the programme. The NMA was performed at key milestones, such as at the end of the phase II trial and prior to the design of key phase III studies. To illustrate the methods, we designed an alternative clinical development programme in multiple sclerosis using RWE through clinical trial simulations. RESULTS: Inclusion of RWE in the NMA and the resulting trial simulations demonstrated that 284 patients per arm were needed to achieve 90% power to detect effects of predetermined size in the TRANSFORMS study. For the FREEDOMS and FREEDOMS II clinical trials, 189 patients per arm were required. Overall there was a reduction in sample size of at least 40% across the three phase III studies, which translated to a time savings of at least 6 months for the undertaking of the fingolimod phase III programme. CONCLUSION: The use of RWE resulted in a reduced sample size of the pivotal phase III studies, which led to substantial time savings compared to the approach of sample size calculations without RWE. |
format | Online Article Text |
id | pubmed-6116448 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61164482018-09-04 The inclusion of real world evidence in clinical development planning Martina, Reynaldo Jenkins, David Bujkiewicz, Sylwia Dequen, Pascale Abrams, Keith Trials Methodology BACKGROUND: When designing studies it is common to search the literature to investigate variability estimates to use in sample size calculations. Proprietary data of previously designed trials in a particular indication are also used to obtain estimates of variability. Estimates of treatment effects are typically obtained from randomised controlled clinical trials (RCTs). Based on the observed estimates of treatment effect, variability and the minimum clinical relevant difference to detect, the sample size for a subsequent trial is estimated. However, data from real world evidence (RWE) studies, such as observational studies and other interventional studies in patients in routine clinical practice, are not widely used in a systematic manner when designing studies. In this paper, we propose a framework for inclusion of RWE in planning of a clinical development programme. METHODS: In our proposed approach, all evidence, from both RCTs and RWE (i.e. from studies in routine clinical practice), available at the time of designing of a new clinical trial is combined in a Bayesian network meta-analysis (NMA). The results can be used to inform the design of the next clinical trial in the programme. The NMA was performed at key milestones, such as at the end of the phase II trial and prior to the design of key phase III studies. To illustrate the methods, we designed an alternative clinical development programme in multiple sclerosis using RWE through clinical trial simulations. RESULTS: Inclusion of RWE in the NMA and the resulting trial simulations demonstrated that 284 patients per arm were needed to achieve 90% power to detect effects of predetermined size in the TRANSFORMS study. For the FREEDOMS and FREEDOMS II clinical trials, 189 patients per arm were required. Overall there was a reduction in sample size of at least 40% across the three phase III studies, which translated to a time savings of at least 6 months for the undertaking of the fingolimod phase III programme. CONCLUSION: The use of RWE resulted in a reduced sample size of the pivotal phase III studies, which led to substantial time savings compared to the approach of sample size calculations without RWE. BioMed Central 2018-08-29 /pmc/articles/PMC6116448/ /pubmed/30157904 http://dx.doi.org/10.1186/s13063-018-2769-2 Text en © The Author(s). 2018 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 | Methodology Martina, Reynaldo Jenkins, David Bujkiewicz, Sylwia Dequen, Pascale Abrams, Keith The inclusion of real world evidence in clinical development planning |
title | The inclusion of real world evidence in clinical development planning |
title_full | The inclusion of real world evidence in clinical development planning |
title_fullStr | The inclusion of real world evidence in clinical development planning |
title_full_unstemmed | The inclusion of real world evidence in clinical development planning |
title_short | The inclusion of real world evidence in clinical development planning |
title_sort | inclusion of real world evidence in clinical development planning |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6116448/ https://www.ncbi.nlm.nih.gov/pubmed/30157904 http://dx.doi.org/10.1186/s13063-018-2769-2 |
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