Bayesian cohort and cross-sectional analyses of the PINCER trial: a pharmacist-led intervention to reduce medication errors in primary care.
BACKGROUND
Medication errors are an important source of potentially preventable morbidity and mortality. The PINCER study, a cluster randomised controlled trial, is one of the world's first experimental studies aiming to reduce the risk of such medication related potential for harm in general practice. Bayesian analyses can improve the clinical interpretability of trial findings.


METHODS
Experts were asked to complete a questionnaire to elicit opinions of the likely effectiveness of the intervention for the key outcomes of interest--three important primary care medication errors. These were averaged to generate collective prior distributions, which were then combined with trial data to generate bayesian posterior distributions. The trial data were analysed in two ways: firstly replicating the trial reported cohort analysis acknowledging pairing of observations, but excluding non-paired observations; and secondly as cross-sectional data, with no exclusions, but without acknowledgement of the pairing. Frequentist and bayesian analyses were compared.


RESULTS
Bayesian evaluations suggest that the intervention is able to reduce the likelihood of one of the medication errors by about 50 (estimated to be between 20% and 70%). However, for the other two main outcomes considered, the evidence that the intervention is able to reduce the likelihood of prescription errors is less conclusive.


CONCLUSIONS
Clinicians are interested in what trial results mean to them, as opposed to what trial results suggest for future experiments. This analysis suggests that the PINCER intervention is strongly effective in reducing the likelihood of one of the important errors; not necessarily effective in reducing the other errors. Depending on the clinical importance of the respective errors, careful consideration should be given before implementation, and refinement targeted at the other errors may be something to consider.
School of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, United Kingdom. k.hemming@bham.ac.uk
Karla
K
Hemming
Peter J
PJ
Chilton
Richard J
RJ
Lilford
Anthony
A
Avery
Aziz
A
Sheikh
D001499:Bayes Theorem; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D004358:Drug Therapy / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D008508:Medication Errors / Q000517:prevention & control* / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D010595:Pharmacists; D011320:Primary Health Care / Q000592:standards* / Q000706:statistics & numerical data; D016032:Randomized Controlled Trials as Topic / Q000592:standards* / Q000706:statistics & numerical data