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|Title: ||Identification of the minimum effective dose for normally distributed data using a Bayesian variable selection approach|
|Authors: ||Otava, Martin|
Hothorn, Ludwig A.
|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.|
|ISI #: ||000419965400012|
|Type: ||Journal Contribution|
|Appears in Collections: ||Research publications|
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