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Recent advances in methodology for clinical trials in small populations: the InSPiRe project
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Number of Authors: 222018 (English)In: Orphanet Journal of Rare Diseases, ISSN 1750-1172, E-ISSN 1750-1172, Vol. 13, article id 186Article, review/survey (Refereed) Published
Abstract [en]

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017. The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.

Place, publisher, year, edition, pages
2018. Vol. 13, article id 186
Keywords [en]
FP7 small populations methodology projects, Statistical methods, Rare disease clinical trial
National Category
Biological Sciences Biomedical Laboratory Science/Technology Information Systems, Social aspects Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-161916DOI: 10.1186/s13023-018-0919-yISI: 000448405400002PubMedID: 30359266OAI: oai:DiVA.org:su-161916DiVA, id: diva2:1262862
Available from: 2018-11-13 Created: 2018-11-13 Last updated: 2020-01-23Bibliographically approved

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