Pharmacovigilance aims to identify adverse drug reactions using postmarket surveillance data under real-world conditions of use. Unlike passive pharmacovigilance, which is based on largely voluntary (and hence incomplete) spontaneous reports of adverse drug reactions with limited information on patient characteristics, active pharmacovigilance is based on electronic health records containing detailed information about patient populations, thereby allowing consideration of modifying factors such as polypharmacy and comorbidity, as well as sociodemographic characteristics. With the present shift toward active pharmacovigilance, statistical methods capable of addressing the complexities of such data are needed. We describe four such methods here, and demonstrate their application in the analysis of a large retrospective cohort of diabetics taking anti-hyperglycemic medications that may increase the risk of adverse cardiovascular events. Copyright

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Keywords Adverse cardiovascular events, Adverse drug reaction, Cox regression, Diabetes, Diabetes drugs, James-Stein shrinkage, Logistic regression, Pharmacovigilance, Random effects
Persistent URL dx.doi.org/10.1080/10543406.2014.901338
Journal Journal of Biopharmaceutical Statistics
Citation
Zhuo, L. (Lan), Farrell, P, McNair, D. (Doug), & Krewski, D. (Daniel). (2014). Statistical methods for active pharmacovigilance, with applications to diabetes drugs. Journal of Biopharmaceutical Statistics, 24(4), 856–873. doi:10.1080/10543406.2014.901338