title: A Bayesian elicitation of veterinary beliefs regarding systemic dry cow therapy: variation and importance for clinical trial design.
abstract: The two key aims of this research were: (i) to conduct a probabilistic elicitation to quantify the variation in veterinarians' beliefs regarding the efficacy of systemic antibiotics when used as an adjunct to intra-mammary dry cow therapy and (ii) to investigate (in a Bayesian statistical framework) the strength of future research evidence required (in theory) to change the beliefs of practising veterinary surgeons regarding the efficacy of systemic antibiotics, given their current clinical beliefs. The beliefs of 24 veterinarians in 5 practices in England were quantified as probability density functions. Classic multidimensional scaling revealed major variations in beliefs both within and between veterinary practices which included: confident optimism, confident pessimism and considerable uncertainty. Of the 9 veterinarians interviewed holding further cattle qualifications, 6 shared a confidently pessimistic belief in the efficacy of systemic therapy and whilst 2 were more optimistic, they were also more uncertain. A Bayesian model based on a synthetic dataset from a randomised clinical trial (showing no benefit with systemic therapy) predicted how each of the 24 veterinarians' prior beliefs would alter as the size of the clinical trial increased, assuming that practitioners would update their beliefs rationally in accordance with Bayes' theorem. The study demonstrated the usefulness of probabilistic elicitation for evaluating the diversity and strength of practitioners' beliefs. The major variation in beliefs observed raises interest in the veterinary profession's approach to prescribing essential medicines. Results illustrate the importance of eliciting prior beliefs when designing clinical trials in order to increase the chance that trial data are of sufficient strength to alter the clinical beliefs of practitioners and do not merely serve to satisfy researchers.
authors.affiliation: School of Mathematical Sciences, University of Nottingham, University Park Campus, Nottingham NG7 2RD, UK. helen.higgins@nottingham.ac.uk
authors.forename: H M
authors.initials: HM
authors.lastname: Higgins
authors.forename: I L
authors.initials: IL
authors.lastname: Dryden
authors.forename: M J
authors.initials: MJ
authors.lastname: Green
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000008:administration & dosage*; D001291:Attitude of Health Personnel; D001499:Bayes Theorem; D002417:Cattle; D004739:England; D005260:Female; D006801:Humans; D007774:Lactation; D008297:Male; D008321:Mammary Glands, Animal / Q000187:drug effects*; D016032:Randomized Controlled Trials as Topic; D018634:Veterinarians / Q000523:psychology*; D014730:Veterinary Medicine / Q000592:standards
chemical_list: D000900:Anti-Bacterial Agents