Detection of safety signals from clinical trial adverse event data is critical in drug development, but carries a challenging statistical multiplicity problem. Bayesian hierarchical mixture modeling is appealing for its ability to borrow strength across subgroups in the data, as well as moderate extreme findings most likely due merely to chance. We implement such a model for subject incidence (Berry and Berry, 2004 Berry , S. , Berry , D. ( 2004 ). Accounting for multiplicities in assessing drug safety: A three-level hierarchical mixture model . Biometrics 60 : 418 – 426 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) using a binomial likelihood, and extend it to subject-year adjusted incidence rate estimation under a Poisson likelihood. We use simulation to choose a signal detection threshold, and illustrate some effective graphics for displaying the flagged signals.