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Determination of the optimal sample size for a clinical trial accounting for the population size
Stockholm University, Faculty of Social Sciences, Department of Statistics.
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2017 (English)In: Biometrical Journal, ISSN 0323-3847, E-ISSN 1521-4036, Vol. 59, no 4, p. 609-625Article in journal (Refereed) Published
Abstract [en]

The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflectthe size of the population under consideration. Incorporation of the population size is possible in adecision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N^1/2) or O(N∗^1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes.

Place, publisher, year, edition, pages
2017. Vol. 59, no 4, p. 609-625
Keywords [en]
Bayesian, Clinical trial design, Decision theory, Exponential family form, Optimal sample size
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-139061DOI: 10.1002/bimj.201500228ISI: 000404907800002OAI: oai:DiVA.org:su-139061DiVA, id: diva2:1070855
Funder
EU, FP7, Seventh Framework Programme, FP HEALTH 2013-602144Available from: 2017-02-02 Created: 2017-02-02 Last updated: 2017-07-28Bibliographically approved

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CiteExportLink to record
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