title: Using Bayesian analysis in repeated preclinical in vivo studies for a more effective use of animals.
abstract: Whilst innovative Bayesian approaches are increasingly used in clinical studies, in the preclinical area Bayesian methods appear to be rarely used in the reporting of pharmacology data. This is particularly surprising in the context of regularly repeated in vivo studies where there is a considerable amount of data from historical control groups, which has potential value. This paper describes our experience with introducing Bayesian analysis for such studies using a Bayesian meta-analytic predictive approach. This leads naturally either to an informative prior for a control group as part of a full Bayesian analysis of the next study or using a predictive distribution to replace a control group entirely. We use quality control charts to illustrate study-to-study variation to the scientists and describe informative priors in terms of their approximate effective numbers of animals. We describe two case studies of animal models: the lipopolysaccharide-induced cytokine release model used in inflammation and the novel object recognition model used to screen cognitive enhancers, both of which show the advantage of a Bayesian approach over the standard frequentist analysis. We conclude that using Bayesian methods in stable repeated in vivo studies can result in a more effective use of animals, either by reducing the total number of animals used or by increasing the precision of key treatment differences. This will lead to clearer results and supports the "3Rs initiative" to Refine, Reduce and Replace animals in research. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: UCB Pharma, 208 Bath Road, Slough, Berks, SL1 3WE, UK.
authors.forename: Rosalind
authors.initials: R
authors.lastname: Walley
authors.affiliation: Statistical contractor, working for UCB Pharma, 208 Bath Road, SL1 3WE, Slough, Berks, UK.
authors.forename: John
authors.initials: J
authors.lastname: Sherington
authors.affiliation: UCB Pharma, 208 Bath Road, Slough, Berks, SL1 3WE, UK.
authors.forename: Joe
authors.initials: J
authors.lastname: Rastrick
authors.affiliation: UCB BioPharma s.p.r.l. Neuroscience Therapeutic Area, Chemin du Foriest, Braine-l'Alleud, Belgium.
authors.forename: Eric
authors.initials: E
authors.lastname: Detrait
authors.affiliation: UCB BioPharma s.p.r.l. Neuroscience Therapeutic Area, Chemin du Foriest, Braine-l'Alleud, Belgium.
authors.forename: Etienne
authors.initials: E
authors.lastname: Hanon
authors.affiliation: UCB Pharma, 208 Bath Road, Slough, Berks, SL1 3WE, UK.
authors.forename: Gillian
authors.initials: G
authors.lastname: Watt
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D016207:Cytokines / Q000378:metabolism; D004195:Disease Models, Animal; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D007249:Inflammation / Q000473:pathology; D008070:Lipopolysaccharides / Q000494:pharmacology; D023421:Models, Animal; D018697:Nootropic Agents / Q000494:pharmacology; D012107:Research Design
chemical_list: D016207:Cytokines; D008070:Lipopolysaccharides; D018697:Nootropic Agents
keywords: Bayesian inference, informative prior, meta-analytic predictive, historical controls, in vivo, 3Rs