期刊
JOURNAL OF BIOPHARMACEUTICAL STATISTICS
卷 21, 期 5, 页码 1006-1029出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10543406.2010.520181
关键词
Bayesian hierarchical models; Clinical trials; Drug safety; Multiplicity; Signal detection
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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