Identification of the minimum effective dose for normally distributed data using a Bayesian variable selection approach. Issue 6 (2nd November 2017)

• Record Type: Journal Article
• Authors: Otava, Martin; Shkedy, Ziv; Hothorn, Ludwig A.; Talloen, Willem; Gerhard, Daniel; Kasim, Adetayo
• Is Part Of: Journal of biopharmaceutical statistics. Volume 27:Issue 6(2017)
• Page Start: 1073
• Page End: 1088
• Publication Date: 2017-11-02
• Subjects: Bayesian variable selection -- minimum effective dose -- model selection -- model uncertainty -- order restricted models; Pharmacy -- Statistical methods -- Periodicals; Drugs -- Testing -- Statistical methods -- Periodicals; Biometry -- Periodicals; Biopharmaceutics -- Periodicals; Pharmacokinetics -- Periodicals; 615.19
• DOI: 10.1080/10543406.2017.1295247
• Languages: English
• ISSNs: 1054-3406

Abstract

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.