| アブストラクト | Spontaneous adverse event reporting systems are widely used to identify adverse reactions to drugs following their introduction into the marketplace. In this article, a James-Stein type shrinkage estimation strategy was developed in a Bayesian logistic regression model to analyze pharmacovigilance data. This method is effective in detecting signals as it combines information and borrows strength across medically related adverse events. Computer simulation demonstrated that the shrinkage estimator is uniformly better than the maximum likelihood estimator in terms of mean squared error. This method was used to investigate the possible association of a series of diabetic drugs and the risk of cardiovascular events using data from the Canada Vigilance Online Database. |
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| ジャーナル名 | Journal of biopharmaceutical statistics |
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| Pubmed追加日 | 2010/8/20 |
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| 投稿者 | An, Lihua; Fung, Karen Y; Krewski, Daniel |
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| 組織名 | McLaughlin Centre for Population Health Risk Assessment, University of Ottawa,;Ottawa, Ontario, Canada. lihuaan@yahoo.com |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/20721787/ |
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