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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26341

Title: Identification of the minimum effective dose for normally distributed data using a Bayesian variable selection approach
Authors: Otava, Martin
Shkedy, Ziv
Hothorn, Ludwig A.
Talloen, Willem
Gerhard, Daniel
Kasim, Adetayo
Issue Date: 2017
Publisher: TAYLOR & FRANCIS INC
Citation: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 27(6), p. 1073-1088
Abstract: The identification of the minimum effective dose is of high importance in the drug development process. In early stage screening experiments, establishing the minimum effective dose can be translated into a model selection based on information criteria. The presented alternative, Bayesian variable selection approach, allows for selection of the minimum effective dose, while taking into account model uncertainty. The performance of Bayesian variable selection is compared with the generalized order restricted information criterion on two dose-response experiments and through the simulations study. Which method has performed better depends on the complexity of the underlying model and the effect size relative to noise.
Notes: [Otava, Martin; Shkedy, Ziv] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Hasselt, Belgium. [Hothorn, Ludwig A.] Leibniz Univ Hannover, Inst Biostat, Hannover, Germany. [Talloen, Willem] Janssen, Beerse, Belgium. [Gerhard, Daniel] Univ Canterbury, Sch Math & Stat, Christchurch, New Zealand. [Kasim, Adetayo] Univ Durham, Wolfson Res Inst Hlth & Wellbeing, Queens Campus,Univ Blvd, Stockton On Tees, England.
URI: http://hdl.handle.net/1942/26341
DOI: 10.1080/10543406.2017.1295247
ISI #: 000419965400012
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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