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Exact Bayesian Inference Comparing Binomial Proportions, With Application to Proof-of-Concept Clinical Trials
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics.
2015 (English)In: THERAPEUTIC INNOVATION & REGULATORY SCIENCE, ISSN 2168-4790, Vol. 49, no 1, 163-174 p.Article in journal (Refereed) Published
Abstract [en]

The authors revisit the problem of exact Bayesian inference comparing two independent binomial proportions. Numerical integration in R is used to compute exact posterior distribution functions, probability densities, and quantiles of the risk difference, relative risk, and odds ratio. An application of the methodology is given in the context of randomized comparative proof-of-concept clinical trials that are driven by evaluation of quantitative criteria combining statistical significance and clinical relevance. A two-stage adaptive design based on predictive probability of success is proposed and its operating characteristics are studied via Monte Carlo simulation. The authors conclude that exact Bayesian methods provide an elegant and efficient way to facilitate design and analysis of proof-of-concept studies.

Place, publisher, year, edition, pages
2015. Vol. 49, no 1, 163-174 p.
Keyword [en]
adaptive design, Bayesian methods, numerical integration, R, proof of concept, two independent binomial proportions
National Category
Pharmacology and Toxicology
URN: urn:nbn:se:uu:diva-245528DOI: 10.1177/2168479014547420ISI: 000347965200022OAI: oai:DiVA.org:uu-245528DiVA: diva2:793939
Available from: 2015-03-09 Created: 2015-02-26 Last updated: 2016-02-17Bibliographically approved

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Ryeznik, Yevgen
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