Synthesizing existing evidence to design future trials: survey of methodologists from European institutions.


Journal

Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253

Informations de publication

Date de publication:
07 Jun 2019
Historique:
received: 06 09 2018
accepted: 13 05 2019
entrez: 9 6 2019
pubmed: 9 6 2019
medline: 3 1 2020
Statut: epublish

Résumé

'Conditional trial design' is a framework for efficiently planning new clinical trials based on a network of relevant existing trials. The framework considers whether new trials are required and how the existing evidence can be used to answer the research question and plan future research. The potential of this approach has not been fully realized. We conducted an online survey among trial statisticians, methodologists, and users of evidence synthesis research using referral sampling to capture opinions about the conditional trial design framework and current practices among clinical researchers. The questions included in the survey were related to the decision of whether a meta-analysis answers the research question, the optimal way to synthesize available evidence, which relates to the acceptability of network meta-analysis, and the use of evidence synthesis in the planning of new studies. In total, 76 researchers completed the survey. Two out of three survey participants (65%) were willing to possibly or definitely consider using evidence synthesis to design a future clinical trial and around half of the participants would give priority to such a trial design. The median rating of the frequency of using such a trial design was 0.41 on a scale from 0 (never) to 1 (always). Major barriers to adopting conditional trial design include the current regulatory paradigm and the policies of funding agencies and sponsors. Participants reported moderate interest in using evidence synthesis methods in the design of future trials. They indicated that a major paradigm shift is required before the use of network meta-analysis is regularly employed in the design of trials.

Sections du résumé

BACKGROUND BACKGROUND
'Conditional trial design' is a framework for efficiently planning new clinical trials based on a network of relevant existing trials. The framework considers whether new trials are required and how the existing evidence can be used to answer the research question and plan future research. The potential of this approach has not been fully realized.
METHODS METHODS
We conducted an online survey among trial statisticians, methodologists, and users of evidence synthesis research using referral sampling to capture opinions about the conditional trial design framework and current practices among clinical researchers. The questions included in the survey were related to the decision of whether a meta-analysis answers the research question, the optimal way to synthesize available evidence, which relates to the acceptability of network meta-analysis, and the use of evidence synthesis in the planning of new studies.
RESULTS RESULTS
In total, 76 researchers completed the survey. Two out of three survey participants (65%) were willing to possibly or definitely consider using evidence synthesis to design a future clinical trial and around half of the participants would give priority to such a trial design. The median rating of the frequency of using such a trial design was 0.41 on a scale from 0 (never) to 1 (always). Major barriers to adopting conditional trial design include the current regulatory paradigm and the policies of funding agencies and sponsors.
CONCLUSIONS CONCLUSIONS
Participants reported moderate interest in using evidence synthesis methods in the design of future trials. They indicated that a major paradigm shift is required before the use of network meta-analysis is regularly employed in the design of trials.

Identifiants

pubmed: 31174597
doi: 10.1186/s13063-019-3449-6
pii: 10.1186/s13063-019-3449-6
pmc: PMC6555919
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

334

Subventions

Organisme : Swiss National Science Foundation
ID : 179158
Organisme : Swiss National Science Foundation
ID : 174281
Organisme : H2020 Marie Skłodowska-Curie Actions
ID : 703254

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Auteurs

Adriani Nikolakopoulou (A)

Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland. nikolakopoulou.adriani@gmail.com.

Sven Trelle (S)

CTU Bern, University of Bern, Bern, Switzerland.

Alex J Sutton (AJ)

Department of Health Sciences, College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester, UK.

Matthias Egger (M)

Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.

Georgia Salanti (G)

Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.

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Classifications MeSH