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A decision theoretical modeling for Phase III investments and drug licensing
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Number of Authors: 22018 (English)In: Journal of Biopharmaceutical Statistics, ISSN 1054-3406, E-ISSN 1520-5711, Vol. 28, no 4, p. 698-721Article in journal (Refereed) Published
Abstract [en]

For a new candidate drug to become an approved medicine, several decision points have to be passed. In this article, we focus on two of them: First, based on Phase II data, the commercial sponsor decides to invest (or not) in Phase III. Second, based on the outcome of Phase III, the regulator determines whether the drug should be granted market access. Assuming a population of candidate drugs with a distribution of true efficacy, we optimize the two stakeholders' decisions and study the interdependence between them. The regulator is assumed to seek to optimize the total public health benefit resulting from the efficacy of the drug and a safety penalty. In optimizing the regulatory rules, in terms of minimal required sample size and the Type I error in Phase III, we have to consider how these rules will modify the commercial optimization made by the sponsor. The results indicate that different Type I errors should be used depending on the rarity of the disease.

Place, publisher, year, edition, pages
2018. Vol. 28, no 4, p. 698-721
Keywords [en]
Clinical trials, drug regulation, optimal Type I error, rare diseases, sample size
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-157844DOI: 10.1080/10543406.2017.1377729ISI: 000434668800008PubMedID: 28920757OAI: oai:DiVA.org:su-157844DiVA, id: diva2:1223907
Available from: 2018-06-26 Created: 2018-06-26 Last updated: 2018-06-26Bibliographically approved

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