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Maximin Efficient Designs for Estimating the Interesting Part of a Dose-Effect Curve
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.
2013 (English)In: 6th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2013):  , 2013Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

As the costs of clinical studies increase, the demand for more efficient designs also increases. Therefore, there is a growing interest in introducing designs that optimize precision in clinical studies. Unfortunately, optimal designs generally require knowledge of unknown parameters. We consider the maximin approach to handle this problem. A maximin efficient design maximizes the efficiency when compared to a standard design, as the parameters vary in a specified subset of the parameter space. 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 an Emax-sigmoid model describing a dose-response relationship and compare inferential precision with that obtained when using a uniform design. In a first approach to construct a maximin efficient design we specify a number of possible scenarios, each of which describing a possible shape of the dose-response relation. The design obtained is shown to be at least 15 percent more efficient than the uniform design. It is then shown that the obtained design is maximin efficient also for a much larger parameter set defined by parameter values between those specified by the initial scenarios.

Place, publisher, year, edition, pages
2013.
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:su:diva-100359OAI: oai:DiVA.org:su-100359DiVA: diva2:692739
Conference
6th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2013) London, 14-16 Dec 2013
Available from: 2014-01-31 Created: 2014-01-31 Last updated: 2014-02-11Bibliographically approved

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Miller, FrankFackle-Fornius, EllinorNyquist, Hans
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CiteExportLink to record
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