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Value of information methods to design a clinical trial in a small population to optimise a health economic utility function
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Number of Authors: 82018 (English)In: BMC Medical Research Methodology, ISSN 1471-2288, E-ISSN 1471-2288, Vol. 18, article id 20Article in journal (Refereed) Published
Abstract [en]

Background: Most confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population. Methods: We considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future. Results: The optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored. Conclusions: Decision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.

Place, publisher, year, edition, pages
2018. Vol. 18, article id 20
Keywords [en]
Decision theory, Health economics, Power, Rare disease, Regulator, Type I error, Value of information
National Category
Public Health, Global Health, Social Medicine and Epidemiology Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:su:diva-153765DOI: 10.1186/s12874-018-0475-0ISI: 000424991600001PubMedID: 29422021OAI: oai:DiVA.org:su-153765DiVA, id: diva2:1192794
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-03-23Bibliographically approved

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