| Bayesian hierarchical modeling for detecting safety signals in clinical trials. | |
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MedLine Citation:
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PMID: 21830928 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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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. |
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Authors:
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H Amy Xia; Haijun Ma; Bradley P Carlin |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Journal of biopharmaceutical statistics Volume: 21 ISSN: 1520-5711 ISO Abbreviation: J Biopharm Stat Publication Date: 2011 Sep |
Date Detail:
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Created Date: 2011-08-11 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9200436 Medline TA: J Biopharm Stat Country: England |
Other Details:
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Languages: eng Pagination: 1006-29 Citation Subset: IM |
Affiliation:
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a Amgen, Inc. , Thousand Oaks , California , USA. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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