SAS Institute, Inc., Cary, North Carolina, USA.
Chin J Nat Med. 2013 May;11(3):314-20. doi: 10.1016/S1875-5364(13)60035-7.
Combine disproportionality analysis with dynamically interactive graphics to understand spontaneously-reported adverse events in pharmacovigilance.
Four statistical methods, including Reporting Odds Ratio, Proportional Reporting Ratio, Multi-Item Gamma Poisson Shrinker and Bayesian Confidence Propagation Neural Network that are used for computing disproportionality are described. Tree maps and other graphical techniques are used to display the disproportionality results.
Spontaneously-reported adverse events in pharmacovigilance are collected from physicians, patients, or the medical literature by regulatory agencies, pharmaceutical companies and device manufacturers to monitor the safety of a product once it reaches the market. In order to identify potential safety-signals, disproportionality analysis methods compare the rate at which a particular event of interest co-occurs with a given drug with the rate this event occurs without the drug in the event database. Tree maps are employed to interactively display the adverse events for particular drugs and compare the adverse events among the drugs.
Interactive graphical displays of disproportionality allow the analyst to quickly identify safety signals and perform additional follow-up analyses. Combining statistical methods with dynamically interactive graphics affords insights into the data inaccessible by traditional analysis methods.
将不均衡分析与动态交互式图形相结合，以了解药物警戒中自发报告的不良事件。
描述了四种用于计算不均衡性的统计方法，包括报告比值比、比例报告比值、多项目伽马泊松收缩器和贝叶斯置信传播神经网络。树图和其他图形技术用于显示不均衡性结果。
药物警戒中的自发报告不良事件由监管机构、制药公司和设备制造商从医生、患者或医学文献中收集，以在产品上市后监测其安全性。为了识别潜在的安全信号，不均衡性分析方法将特定感兴趣事件与特定药物同时发生的速率与该事件在事件数据库中无药物时发生的速率进行比较。树图用于交互式显示特定药物的不良事件，并比较药物之间的不良事件。
不均衡性的交互式图形显示使分析师能够快速识别安全信号并进行额外的后续分析。将统计方法与动态交互式图形相结合，可以深入了解传统分析方法无法获取的数据。
