Department of Mathematics and Statistics, University of South Florida, 4202 East Fowler Ave, CMC342, Tampa, FL, 33620, USA.
Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, 12901 Bruce B. Downs Blvd, MDC 30, Tampa, FL, 33612, USA.
Drug Saf. 2019 Jun;42(6):743-750. doi: 10.1007/s40264-018-00792-0.
It is important to monitor the safety profile of drugs, and mining for strong associations between drugs and adverse events is an effective and inexpensive method of post-marketing safety surveillance.
The objective of our work was to compare the accuracy of both common and innovative methods of data mining for pharmacovigilance purposes.
We used the reference standard provided by the Observational Medical Outcomes Partnership, which contains 398 drug-adverse event pairs (165 positive controls, 233 negative controls). Ten methods and algorithms were applied to the US FDA Adverse Event Reporting System data to investigate the 398 pairs. The ten methods include popular methods in the pharmacovigilance literature, newly developed pharmacovigilance methods as at 2018, and popular methods in the genome-wide association study literature. We compared their performance using the receiver operating characteristic (ROC) plot, area under the curve (AUC), and Youden's index.
The Bayesian confidence propagation neural network had the highest AUC overall. Monte Carlo expectation maximization, a method developed in 2018, had the second highest AUC and the highest Youden's index, and performed very well in terms of high specificity. The regression-adjusted gamma Poisson shrinkage model performed best under high-sensitivity requirements.
Our results will be useful to help choose a method for a given desired level of specificity. Methods popular in the genome-wide association study literature did not perform well because of the sparsity of data and will need modification before their properties can be used in the drug-adverse event association problem.
监测药物安全性状况十分重要，而挖掘药物与不良事件之间的强关联是一种有效的、低成本的上市后安全监测方法。
我们的工作旨在比较数据挖掘在药物警戒方面的常用方法和创新方法的准确性。
我们使用了观察性医学结局伙伴关系提供的参考标准，其中包含 398 对药物-不良事件（165 个阳性对照，233 个阴性对照）。我们应用了 10 种方法和算法对美国食品药品监督管理局不良事件报告系统数据进行了调查，以研究这 398 对药物-不良事件。这 10 种方法包括药物警戒文献中的流行方法、截至 2018 年新开发的药物警戒方法以及全基因组关联研究文献中的流行方法。我们使用接收者操作特征（ROC）图、曲线下面积（AUC）和 Youden 指数比较了它们的性能。
贝叶斯置信传播神经网络的 AUC 总体最高。蒙特卡罗期望最大化，一种 2018 年开发的方法，具有第二高的 AUC 和最高的 Youden 指数，并且在高特异性方面表现非常出色。回归调整伽马泊松收缩模型在高灵敏度要求下表现最佳。
我们的结果将有助于根据特定的特异性要求选择方法。全基因组关联研究文献中流行的方法由于数据稀疏性而表现不佳，在其特性可用于药物-不良事件关联问题之前需要进行修改。
