Almenoff J S, Pattishall E N, Gibbs T G, DuMouchel W, Evans S J W, Yuen N
Department of Epidemiology and Population Health, Safety Evaluation and Risk Management, Global Clinical Safety and Pharmacovigilance, GlaxoSmithKline, Research Triangle Park, North Carolina, USA.
Clin Pharmacol Ther. 2007 Aug;82(2):157-66. doi: 10.1038/sj.clpt.6100258. Epub 2007 May 30.
Robust tools for monitoring the safety of marketed therapeutic products are of paramount importance to public health. In recent years, innovative statistical approaches have been developed to screen large post-marketing safety databases for adverse events (AEs) that occur with disproportionate frequency. These methods, known variously as quantitative signal detection, disproportionality analysis, or safety data mining, facilitate the identification of new safety issues or possible harmful effects of a product. In this article, we describe the statistical concepts behind these methods, as well as their practical application to monitoring the safety of pharmaceutical products using spontaneous AE reports. We also provide examples of how these tools can be used to identify novel drug interactions and demographic risk factors for adverse drug reactions. Challenges, controversies, and frontiers for future research are discussed.
用于监测已上市治疗产品安全性的可靠工具对公众健康至关重要。近年来,已开发出创新的统计方法,以筛查大型上市后安全性数据库中发生频率不成比例的不良事件(AE)。这些方法有多种称呼,如定量信号检测、不成比例分析或安全数据挖掘,有助于识别产品新的安全问题或可能的有害影响。在本文中,我们描述了这些方法背后的统计概念,以及它们在利用自发AE报告监测药品安全性方面的实际应用。我们还提供了这些工具如何用于识别新型药物相互作用和药物不良反应的人口统计学风险因素的示例。文中讨论了未来研究面临的挑战、争议和前沿问题。