Nomura Kaori, Takahashi Kunihiko, Hinomura Yasushi, Kawaguchi Genta, Matsushita Yasuyuki, Marui Hiroko, Anzai Tatsuhiko, Hashiguchi Masayuki, Mochizuki Mayumi
Division of Molecular Epidemiology, Jikei University School of Medicine, Tokyo, Japan.
Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Drug Des Devel Ther. 2015 Jun 12;9:3031-41. doi: 10.2147/DDDT.S81998. eCollection 2015.
The use of a statistical approach to analyze cumulative adverse event (AE) reports has been encouraged by regulatory authorities. However, data variations affect statistical analyses (eg, signal detection). Further, differences in regulations, social issues, and health care systems can cause variations in AE data. The present study examined similarities and differences between two publicly available databases, ie, the Japanese Adverse Drug Event Report (JADER) database and the US Food and Drug Administration Adverse Event Reporting System (FAERS), and how they affect signal detection.
Two AE data sources from 2010 were examined, ie, JADER cases (JP) and Japanese cases extracted from the FAERS (FAERS-JP). Three methods for signals of disproportionate reporting, ie, the reporting odds ratio, Bayesian confidence propagation neural network, and Gamma Poisson Shrinker (GPS), were used on drug-event combinations for three substances frequently recorded in both systems.
The two databases showed similar elements of AE reports, but no option was provided for a shareable case identifier. The average number of AEs per case was 1.6±1.3 (maximum 37) in the JP and 3.3±3.5 (maximum 62) in the FAERS-JP. Between 5% and 57% of all AEs were signaled by three quantitative methods for etanercept, infliximab, and paroxetine. Signals identified by GPS for the JP and FAERS-JP, as referenced by Japanese labeling, showed higher positive sensitivity than was expected.
The FAERS-JP was different from the JADER. Signals derived from both datasets identified different results, but shared certain signals. Discrepancies in type of AEs, drugs reported, and average number of AEs per case were potential contributing factors. This study will help those concerned with pharmacovigilance better understand the use and pitfalls of using spontaneous AE data.
监管机构鼓励采用统计方法分析累积不良事件（AE）报告。然而，数据差异会影响统计分析（如信号检测）。此外，法规、社会问题和医疗保健系统的差异可能导致AE数据的变化。本研究考察了两个公开可用数据库，即日本药品不良反应报告（JADER）数据库和美国食品药品监督管理局不良事件报告系统（FAERS）之间的异同，以及它们如何影响信号检测。
研究了2010年的两个AE数据源，即JADER病例（JP）和从FAERS中提取的日本病例（FAERS-JP）。对两个系统中经常记录的三种物质的药物-事件组合，使用三种不成比例报告信号的方法，即报告比值比、贝叶斯置信传播神经网络和伽马泊松收缩器（GPS）。
两个数据库显示出AE报告的相似元素，但未提供可共享的病例标识符选项。JP中每个病例的AE平均数量为1.6±1.3（最大37），FAERS-JP中为3.3±3.5（最大62）。三种定量方法对依那西普、英夫利昔单抗和帕罗西汀的所有AE中有5%至57%发出了信号。GPS在JP和FAERS-JP中识别出的信号，以日本标签为参考，显示出比预期更高的阳性敏感性。
FAERS-JP与JADER不同。从两个数据集得出的信号识别出不同的结果，但有一些共同的信号。AE类型、报告的药物以及每个病例的AE平均数量的差异是潜在的影响因素。本研究将有助于关注药物警戒的人员更好地理解使用自发AE数据的方法和陷阱。
