Identification of the minimum effective dose for normally distributed data using a Bayesian variable selection approach.
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.
a Interuniversity Institute for Biostatistics and Statistical Bioinformatics , Hasselt University , Hasselt , Belgium.
Martin
M
Otava
a Interuniversity Institute for Biostatistics and Statistical Bioinformatics , Hasselt University , Hasselt , Belgium.
Ziv
Z
Shkedy
b Institute of Biostatistics , Leibniz University Hannover, Hannover, Germany.
Ludwig A
LA
Hothorn
c Janssen, Pharmaceutical companies of Johnson & Johnson , Beerse , Belgium.
Willem
W
Talloen
d School of Mathematics and Statistics , University of Canterbury , Christchurch , New Zealand.
Daniel
http://orcid.org/0000-0002-9336-3454
D
Gerhard
e Wolfson Research Institute for Health and Wellbeing , Durham University, Queen's Campus, University Boulevard , Stockton-on-Tees , United Kingdom.
Adetayo
A
Kasim
D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016011:Normal Distribution; D035501:Uncertainty
Bayesian variable selection; minimum effective dose; model selection; model uncertainty; order restricted models