Zhuo Lan, Farrell Patrick J, McNair Doug, Krewski Daniel
a School of Mathematics and Statistics , Carleton University , Ottawa , Ontario , Canada.
J Biopharm Stat. 2014;24(4):856-73. doi: 10.1080/10543406.2014.901338.
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.
药物警戒旨在利用实际使用情况下的上市后监测数据来识别药物不良反应。与被动药物警戒不同，被动药物警戒主要基于自愿（因此不完整）的药物不良反应自发报告，且关于患者特征的信息有限，而主动药物警戒基于包含患者群体详细信息的电子健康记录，从而能够考虑诸如联合用药和合并症等修正因素，以及社会人口学特征。随着目前向主动药物警戒的转变，需要能够处理此类数据复杂性的统计方法。我们在此描述四种这样的方法，并展示它们在分析大量服用可能增加不良心血管事件风险的降糖药物的糖尿病患者回顾性队列中的应用。
