Real-world evidence for coverage determination of treatments for rare diseases

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Dayer, V.W.; Drummond, M.F.; Dabbous, O.; Toumi, M.; Neumann, P. et al.: Real-world evidence for coverage determination of treatments for rare diseases. In: Orphanet Journal of Rare Diseases 19 (2024), Nr. 1, 47. DOI: https://doi.org/10.1186/s13023-024-03041-z

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Sum total of downloads: 11




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Abstract: 
Health technology assessment (HTA) decisions for pharmaceuticals are complex and evolving. New rare disease treatments are often approved more quickly through accelerated approval schemes, creating more uncertainties about clinical evidence and budget impact at the time of market entry. The use of real-world evidence (RWE), including early coverage with evidence development, has been suggested as a means to support HTA decisions for rare disease treatments. However, the collection and use of RWE poses substantial challenges. These challenges are compounded when considered in the context of treatments for rare diseases. In this paper, we describe the methodological challenges to developing and using prospective and retrospective RWE for HTA decisions, for rare diseases in particular. We focus attention on key elements of study design and analyses, including patient selection and recruitment, appropriate adjustment for confounding and other sources of bias, outcome selection, and data quality monitoring. We conclude by offering suggestions to help address some of the most vexing challenges. The role of RWE in coverage and pricing determination will grow. It is, therefore, necessary for researchers, manufacturers, HTA agencies, and payers to ensure that rigorous and appropriate scientific principles are followed when using RWE as part of decision-making.
License of this version: CC BY 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2024
Appears in Collections:Wirtschaftswissenschaftliche Fakultät

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pos. country downloads
total perc.
1 image of flag of United States United States 4 36.36%
2 image of flag of Germany Germany 4 36.36%
3 image of flag of No geo information available No geo information available 1 9.09%
4 image of flag of United Kingdom United Kingdom 1 9.09%
5 image of flag of China China 1 9.09%

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