Ahmed Ismaïl, Haramburu Françoise, Fourrier-Réglat Annie, Thiessard Frantz, Kreft-Jais Carmen, Miremont-Salamé Ghada, Bégaud Bernard, Tubert-Bitter Pascale
Inserm, U780, 16 Avenue Paul Vaillant Couturier, Villejuif F-94807, France.
Stat Med. 2009 Jun 15;28(13):1774-92. doi: 10.1002/sim.3586.
Pharmacovigilance spontaneous reporting systems are primarily devoted to early detection of the adverse reactions of marketed drugs. They maintain large spontaneous reporting databases (SRD) for which several automatic signalling methods have been developed. A common limitation of these methods lies in the fact that they do not provide an auto-evaluation of the generated signals so that thresholds of alerts are arbitrarily chosen. In this paper, we propose to revisit the Gamma Poisson Shrinkage (GPS) model and the Bayesian Confidence Propagation Neural Network (BCPNN) model in the Bayesian general decision framework. This results in a new signal ranking procedure based on the posterior probability of null hypothesis of interest and makes it possible to derive with a non-mixture modelling approach Bayesian estimators of the false discovery rate (FDR), false negative rate, sensitivity and specificity. An original data generation process that can be suited to the features of the SRD under scrutiny is proposed and applied to the French SRD to perform a large simulation study. Results indicate better performances according to the FDR for the proposed ranking procedure in comparison with the current ones for the GPS model. They also reveal identical performances according to the four operating characteristics for the proposed ranking procedure with the BCPNN and GPS models but better estimates when using the GPS model. Finally, the proposed procedure is applied to the French data.
药物警戒自发报告系统主要致力于上市药物不良反应的早期发现。它们维护着大型自发报告数据库（SRD），并已开发出多种自动信号检测方法。这些方法的一个共同局限在于，它们无法对生成的信号进行自动评估，因此警报阈值是任意选定的。在本文中，我们建议在贝叶斯通用决策框架下重新审视伽马泊松收缩（GPS）模型和贝叶斯置信传播神经网络（BCPNN）模型。这产生了一种基于感兴趣的零假设的后验概率的新信号排序程序，并使得有可能通过非混合建模方法得出错误发现率（FDR）、假阴性率、灵敏度和特异性的贝叶斯估计量。我们提出了一种能够适应所审查的SRD特征的原始数据生成过程，并将其应用于法国的SRD以进行大规模模拟研究。结果表明，与当前GPS模型的方法相比，所提出的排序程序在FDR方面表现更优。结果还显示，所提出的排序程序在BCPNN和GPS模型的四种操作特征方面表现相同，但使用GPS模型时估计效果更好。最后，将所提出的程序应用于法国的数据。
