Mining pharmacovigilance data using Bayesian logistic regression with James-Stein type shrinkage estimation.
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.
An
Lihua
L
McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada. lihuaan@yahoo.com
Fung
Karen Y
KY
Krewski
Daniel
D
D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods*; D002170:Canada; D002318:Cardiovascular Diseases / Q000139:chemically induced; D003198:Computer Simulation; D057225:Data Mining; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical
D007004:Hypoglycemic Agents