Huang Lan, Guo Ted, Zalkikar Jyoti N, Tiwari Ram C
1 Division of Biostatistics V, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
2 Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA.
Ther Innov Regul Sci. 2014 Jan;48(1):98-108. doi: 10.1177/2168479013514236.
The data-mining statistical methods used for disproportionality analysis of drug-adverse event combinations from large drug safety databases such as the FDA's Adverse Event Reporting System (FAERS), consisting of spontaneous reports on adverse events for postmarket drugs, are called passive surveillance methods. However, the statistical signal detection methods for longitudinal data, as the data accrue in time, are called active surveillance methods. A review of the most commonly used passive surveillance statistical methods and the relationships among them is presented with unified notations. These methods are applied to the 2006-2012 FAERS data; the number of drug signals of disproportionate rates (SDRs) detected by each of these methods with the common SDRs from all of these methods, for the adverse event myocardial infarction, are given. Finally, there is a brief discussion on the recently developed active surveillance methods.
用于对来自大型药物安全数据库(如美国食品药品监督管理局的不良事件报告系统(FAERS),该系统由上市后药物不良事件的自发报告组成)中的药物不良事件组合进行不成比例分析的数据挖掘统计方法,被称为被动监测方法。然而,随着数据随时间积累而用于纵向数据的统计信号检测方法,则被称为主动监测方法。本文用统一的符号表示法对最常用的被动监测统计方法及其相互关系进行了综述。这些方法应用于2006 - 2012年的FAERS数据;给出了每种方法检测到的比例失调率(SDR)药物信号数量以及所有这些方法针对不良事件心肌梗死的共同SDR。最后,对最近开发的主动监测方法进行了简要讨论。