title: An approximate Bayesian risk-analysis for the gastro-intestinal safety of ibuprofen.
abstract: OBJECTIVE
  Although several studies on ibuprofen and its gastro-intestinal (GI) risk have been reported, the dose-response relationship was not clear due to the lack of information regarding high-dose exposure. Analysis using Bayesian methods is appropriate whenever data are sparse, although such methods are not easily implemented.
  
  
  METHODS
  A retrospective cohort study to assess this dose-response relationship was carried out using a record linkage database. A Bayesian risk-analysis was conducted using the Bayesian bootstrap approximation. Risks of GI events at different dose levels were compared using the posterior distributions and the number of events predicted to occur in the future was estimated. Risk factors such as age, gender and co-morbidity were adjusted for in the analysis. This approximation was compared with the full Bayesian approach using the usual but more computer-intensive tool of Markov Chain Monte Carlo simulation.
  
  
  RESULTS
  There were 1, 5 and 10 complicated GI events during exposure to high, medium and low dose ibuprofen with 0.2, 1.8 and 7.0 thousand person-years (PY) exposure, respectively. After adjusting for other risk factors the relative risks of high versus low and medium versus low doses were 6.3 (95% CI = 0.21, 24.17) and 2.5 (95% CI = 0.71, 5.85), respectively. Using the approximate Bayesian method prediction of the number of events in a population of females aged 50-59 with no previous medical problems with 1000 PY drug exposure showed that the estimated probability of having more than five events was 0.048 for the medium-dose group and 0.14 for the high-dose group.
  
  
  CONCLUSIONS
  High dose ibuprofen appears to have a considerably greater risk of having a larger number of adverse GI events than a medium dose. The approximate Bayesian bootstrap method was demonstrated to be a robust and easily implemented alternative to the full Bayesian approach to risk analysis whenever data are sparse.
authors.affiliation: Medicines Monitoring Unit, Department of Pharmacology and Therapeutics, University of Dundee, Dundee DD1 9SY, Scotland.
authors.forename: Jixian
authors.initials: J
authors.lastname: Wang
authors.forename: Peter T
authors.initials: PT
authors.lastname: Donnan
authors.forename: Thomas M
authors.initials: TM
authors.lastname: MacDonald
mesh_terms: D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects*; D001499:Bayes Theorem; D015331:Cohort Studies; D004064:Digestive System / Q000187:drug effects*; D004305:Dose-Response Relationship, Drug; D005260:Female; D006801:Humans; D007052:Ibuprofen / Q000009:adverse effects*; D008297:Male; D012189:Retrospective Studies; D012606:Scotland
chemical_list: D000894:Anti-Inflammatory Agents, Non-Steroidal; D007052:Ibuprofen