Time-to-Signal Comparison for Drug Safety Data-Mining Algorithms vs. Traditional Signaling Criteria
Time-to-Signal Comparison for Drug Safety Data-Mining Algorithms vs. Traditional Signaling Criteria
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DOI：
10.1038/clpt.2009.26
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发表时间：
2009-06-01
影响因子：
6.7
通讯作者：
Hauben, M.
中科院分区：
文献类型：
--
作者：
Hochberg, A. M.;Hauben, M.
数据挖掘可以改善信号识别，但其增量效用值得怀疑。本研究的目的是比较数据挖掘强调的关联与使用传统决策规则强调的关联。对于 29 种药物，我们使用美国食品和药物管理局 (FDA) 不良事件报告系统 (AERS) 数据将三种数据挖掘算法 (DMA) 与两种传统决策规则进行比较：(i) N >= 3 份针对指定医疗事件 (DME) 的报告，以及 (ii) 占与药物相关的报告的 2% 以上的任何事件。数据挖掘方法产生了 101-324 个信号，而 N >= 3 规则则产生了 1,051 个信号，但产生了更高比例的具有出版物支持的信号。对于 2% 规则，具有出版物支持的信号比例与与数据挖掘相关的信号比例相似。数据挖掘信号滞后 N >= 3 信号 1.5-11.0 个月。因此可以得出结论，数据挖掘识别的信号少于“N >= 3 DME”规则。这些信号后来通过数据挖掘出现，但更经常得到出版物的支持。在 2% 规则的情况下，没有观察到出版支持方面的这种差异。
Data mining may improve identification of signals, but its incremental utility is in question. The objective of this study was to compare associations highlighted by data mining vs. those highlighted through the use of traditional decision rules. In the case of 29 drugs, we used US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) data to compare three data-mining algorithms (DMAs) with two traditional decision rules: (i) N >= 3 reports for a designated medical event (DME) and (ii) any event comprising >2% of reports in relation to a drug. Data-mining methods produced 101-324 signals vs. 1,051 for the N >= 3 rule but yielded a higher proportion of signals having publication support. For the 2% rule, the fraction of signals having publication support was similar to that associated with data mining. Data-mining signals lagged N >= 3 signaling by 1.5-11.0 months. It may therefore be concluded that data mining identifies fewer signals than the "N >= 3 DME" rule. The signals appear later with data mining but are more often supported by publications. In the case of the 2% rule, no such difference in publication support was observed.