Bayesian hierarchical modeling for detecting safety signals in clinical trials

H. Amy Xia, Haijun Ma, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1006-1029
Number of pages24
JournalJournal of Biopharmaceutical Statistics
Volume21
Issue number5
DOIs
StatePublished - Sep 1 2011

Keywords

  • Bayesian hierarchical models
  • Clinical trials
  • Drug safety
  • Multiplicity
  • Signal detection

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