Extending the methods used to screen the WHO drug safety database towards analysis of complex associations and improved accuracy for rare events.

Post-marketing drug safety data sets are often massive, and entail problems with heterogeneity and selection bias. Nevertheless, quantitative methods have proven a very useful aid to help clinical experts in screening for previously unknown associations in these data sets. The WHO international drug safety database is the world's largest data set of its kind with over three million reports on suspected adverse drug reaction incidents. Since 1998, an exploratory data analysis method has been in routine use to screen for quantitative associations in this data set. This method was originally based on large sample approximations and limited to pairwise associations, but in this article we propose more accurate credibility interval estimates and extend the method to allow for the analysis of more complex quantitative associations. The accuracy of the proposed credibility intervals is evaluated through comparison to precise Monte Carlo simulations. In addition, we propose a Mantel-Haenszel-type adjustment to control for suspected confounders.