Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance
Evaluation of statistical association measures for the automatic signal generation in pharmacovigilance
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DOI：
10.1109/titb.2005.855566a
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发表时间：
2005-12-01
影响因子：
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通讯作者：
Tubert-Bitter, P
中科院分区：
文献类型：
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作者：
Roux, E;Thiessard, F;Tubert-Bitter, P
药物警戒的目的是检测上市药物的不良反应。它通常是基于对被认为是药物不良影响的事件的自发报告。自发报告系统(SRSS)提供了巨大的数据库，如果没有数据挖掘工具，药物警戒专家就无法穷尽地利用这些数据库。在文献中已经提出了数据挖掘方法，即结合信号生成标准的统计关联措施，但对于它们的适用性和效率没有达成共识，特别是因为这种方法很难基于实际数据进行评估。本文的目的是对通过SRS建模获得的模拟数据集的关联度量进行评估。我们根据关联度的值，使用给定数量的最高排名的药物-事件组合中的假阳性信号百分比来比较关联度。考虑到150种药物和100种不良事件，在排名最高的500对药物事件夫妇中，假阳性的百分比从1.1%到53.4%不等(平均超过1000个模拟数据集)。由于这些措施导致了非常不同的结果，我们可以确定哪些措施似乎与药物警戒最相关。
Pharmacovigilance aims at detecting the adverse effects of marketed drugs. It is generally based on the spontaneous reporting of events thought to be the adverse effects of drugs. Spontaneous Reporting Systems (SRSs) supply huge databases that pharmacovigilance experts cannot exhaustively exploit without data mining tools. Data mining methods; i.e., statistical association measures in conjunction with signal generation criteria, have been proposed in the literature but there is no consensus regarding their applicability and efficiency, especially since such methods are difficult to evaluate on the basis of actual data. The objective of this paper is to evaluate association measures on simulated datasets obtained with SRS modeling. We compared association measures using the percentage of false positive signals among a given number of the most highly ranked drug-event combinations according to the values of the association measures. By considering 150 drugs and 100 adverse events, these percentages of false positives, among the 500 most highly ranked drug-event couples, vary from 1.1% to 53.4% (averages over 1000 simulated datasets). As the measures led to very different results, we could identify which measures appeared to be the most relevant for pharmacovigilance.