A Bayesian analysis of a mastitis control plan to investigate the influence of veterinary beliefs on clinical decisions

A fundamental reason to evaluate animal health data is to determine whether changes should be made to clinical decisions. However, decisions made by veterinarians in the light of new research are known to be influenced by their original (prior) beliefs. In this research, clinical trial results for a bovine mastitis control plan were evaluated within a Bayesian context, to incorporate a community of prior distributions that represented a spectrum of clinical beliefs. The aim was to quantify differences in interpretation likely to be made by veterinarians with different initial viewpoints given the trial results. A Bayesian analysis was conducted using Markov chain Monte Carlo procedures. Stochastic models included a financial cost attributed to a change in mastitis following implementation of the control plan. Prior distributions covered a realistic range of clinical viewpoints, including scepticism, enthusiasm and uncertainty in the efficacy of the control plan. Results revealed dramatic differences in the financial gain that clinicians with different starting viewpoints would expect from the mastitis control plan, given the actual research results; e.g. a severe sceptic would expect a return of <£5 per cow in an average herd that implemented the plan, whereas an enthusiast would expect >£20 per cow. Simulations using theoretical future trials indicated that after three further equivalent studies, an initial sceptic would still expect substantially less return from the control plan than an initial enthusiast would expect after the first trial. In conclusion, it is possible to quantify how clinicians' prior beliefs influence the interpretation of research and therefore their likely approach to mastitis control.