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Implementation of maximin efficient designs in dose-finding studies
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2015 (English)In: Pharmaceutical statistics, ISSN 1539-1604, E-ISSN 1539-1612, Vol. 14, no 1, 63-73 p.Article in journal (Refereed) Published
Abstract [en]

This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four-step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose-finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H-algorithm, considerably simplifies the construction of these designs.We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose–response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design.

Place, publisher, year, edition, pages
2015. Vol. 14, no 1, 63-73 p.
Keyword [en]
clinical study, dose–response model, extension of parameter set, H-algorithm, maximin efficient design, optimal design
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-110796DOI: 10.1002/pst.1660ISI: 000348520200009OAI: oai:DiVA.org:su-110796DiVA: diva2:772888
Funder
Swedish Research Council
Available from: 2014-12-17 Created: 2014-12-17 Last updated: 2017-12-05Bibliographically approved

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