Hauben Manfred, Reich Lester, Chung Stephanie
Pfizer Inc, 150 E. 42nd Street, New York, NY 10017, USA.
Eur J Clin Pharmacol. 2004 Dec;60(10):747-50. doi: 10.1007/s00228-004-0834-0. Epub 2004 Nov 17.
Several data mining algorithms (DMAs) are being studied in hopes of enhancing screening of large post-marketing safety databases for signals of novel adverse events (AEs). The objective of this study was to apply two DMAs to the United States FDA Adverse Event Reporting System (AERS) database to see whether signals of potentially fatal AEs with cancer drugs might have been identified earlier than with traditional methods.
Screening algorithms used for analysis were the multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratios (PRRs). Data mining was performed on data from the FDA AERS database. When a signal was identified, it was compared with that in the year in which the event was added to package insert and/or the year a "case series" was published. A recent publication summarizing the time of dissemination of information on potentially fatal AEs to cancer drugs provided the data set for analysis.
The peer-reviewed published analysis contained 21 drugs and 26 drug-event combinations (DECs) that were considered sufficiently specific for data mining. Twenty-four of the DECs generated a signal of disproportionate reporting with PRRs (6 at 1 year and 16 from 2 years to 18 years prior to either a published "case series" or a package insert change) and 20 with MGPS (3 at 1 year and 11 from 2 years to 16 years prior to either a published "case series" or a package insert change). Two DECs did not signal with either DMA.
At least one commonly cited DMA generated a signal of disproportionate reporting for 24 of 26 DECs for selected cancer drugs. For 16 DECs, one could conclude that a signal was generated well in advance (> or =2 years) of standard techniques in use with at least one DMA. DMAs might be useful in supplementing traditional surveillance strategies with oncology drugs and other drugs with similar features. (i.e., drugs that may be approved on an accelerated basis, are known to have serious toxicity, are administered to patients with substantial and complicated comorbid illness, are not available to the general medical community, and may have a high frequency of "off-label" use).
正在研究几种数据挖掘算法（DMA），以期加强对大型上市后安全数据库的筛查，以发现新的不良事件（AE）信号。本研究的目的是将两种DMA应用于美国食品药品监督管理局不良事件报告系统（AERS）数据库，以查看与传统方法相比，是否能更早地识别出癌症药物潜在致命AE的信号。
用于分析的筛查算法为多项目伽马泊松收缩器（MGPS）和比例报告比（PRR）。对FDA AERS数据库中的数据进行数据挖掘。当识别出一个信号时，将其与事件添加到药品说明书的年份和/或“病例系列”发表的年份进行比较。最近一篇总结向癌症药物传播潜在致命AE信息时间的出版物提供了分析数据集。
同行评审发表的分析包含21种药物和26种药物-事件组合（DEC），这些被认为对数据挖掘具有足够的特异性。24种DEC通过PRR产生了不成比例报告的信号（在发表“病例系列”或药品说明书变更前1年有6种，在2年至18年有16种），20种通过MGPS产生信号（在发表“病例系列”或药品说明书变更前1年有3种，在2年至16年有11种）。两种DEC未通过任何一种DMA产生信号。
对于选定的癌症药物，至少一种常用的DMA对26种DEC中的24种产生了不成比例报告的信号。对于16种DEC，可以得出结论，至少有一种DMA在标准技术使用之前（≥2年）就产生了信号。DMA可能有助于补充肿瘤药物和其他具有类似特征药物（即可能加速获批、已知具有严重毒性、用于患有大量复杂合并症的患者、普通医学界无法使用且可能有高频率“超说明书”使用的药物）的传统监测策略。
