Analysis of a Bayesian repeated measures model for detecting differences in GP prescribing habits.
A linear mixed model is used to detect a change, if any, in the prescribing habits in the UK at the general practice (family medicine) level due to an educational intervention given repeated measures data before and after the intervention and a control group. Inferences are corrected for general practice size and fundholding status. The estimates of the model parameters are obtained using Bayesian inference by applying Gibbs sampling. We develop three different priors for the parameters of the model. These three priors correspond to 'sceptical,' 'reference' and 'enthusiastic' priors in terms of the opinion about the treatment effects that they represent. We compare the results obtained by using these three priors for the parameters in the random effects model.
Sithole
Jabu S
JS
MRC Institute of Hearing Research, University Park, Nottingham, NG7 2RD, UK. j.sithole@ihr.mrc.ac.uk
Jones
Peter W
PW
D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000627:therapeutic use; D001499:Bayes Theorem; D004363:Drug Utilization / Q000706:statistics & numerical data* / Q000639:trends; D005194:Family Practice / Q000193:education* / Q000592:standards; D006801:Humans; D007052:Ibuprofen / Q000627:therapeutic use; D016014:Linear Models; D008137:Longitudinal Studies; D009010:Monte Carlo Method; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data* / Q000639:trends; D006113:United Kingdom
D000894:Anti-Inflammatory Agents, Non-Steroidal; D007052:Ibuprofen