title: Bayesian hierarchical modeling for detecting safety signals in clinical trials.
abstract: 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 ) 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.
authors.affiliation: Amgen, Inc., Thousand Oaks, California 91320, USA. hxia@amgen.com
authors.forename: H Amy
authors.initials: HA
authors.lastname: Xia
authors.forename: Haijun
authors.initials: H
authors.lastname: Ma
authors.forename: Bradley P
authors.initials: BP
authors.lastname: Carlin
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data* / Q000639:trends; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D003198:Computer Simulation / Q000706:statistics & numerical data; D004018:Dictionaries, Medical as Topic; D004345:Drug Industry / Q000379:methods / Q000706:statistics & numerical data* / Q000639:trends; D064420:Drug-Related Side Effects and Adverse Reactions; D005189:False Positive Reactions; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D060735:Pharmacovigilance; D011336:Probability; D012044:Regression Analysis; D012449:Safety; D012984:Software / Q000706:statistics & numerical data*; D014481:United States; D014486:United States Food and Drug Administration