Pieces Technology, 8435 N Stemmons Fwy #1150, Dallas, TX, USA.
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
BMC Med Inform Decis Mak. 2017 Jul 5;17(Suppl 2):76. doi: 10.1186/s12911-017-0472-y.
To identify safety signals by manual review of individual report in large surveillance databases is time consuming; such an approach is very unlikely to reveal complex relationships between medications and adverse events. Since the late 1990s, efforts have been made to develop data mining tools to systematically and automatically search for safety signals in surveillance databases. Influenza vaccines present special challenges to safety surveillance because the vaccine changes every year in response to the influenza strains predicted to be prevalent that year. Therefore, it may be expected that reporting rates of adverse events following flu vaccines (number of reports for a specific vaccine-event combination/number of reports for all vaccine-event combinations) may vary substantially across reporting years. Current surveillance methods seldom consider these variations in signal detection, and reports from different years are typically collapsed together to conduct safety analyses. However, merging reports from different years ignores the potential heterogeneity of reporting rates across years and may miss important safety signals.
Reports of adverse events between years 1990 to 2013 were extracted from the Vaccine Adverse Event Reporting System (VAERS) database and formatted into a three-dimensional data array with types of vaccine, groups of adverse events and reporting time as the three dimensions. We propose a random effects model to test the heterogeneity of reporting rates for a given vaccine-event combination across reporting years. The proposed method provides a rigorous statistical procedure to detect differences of reporting rates among years. We also introduce a new visualization tool to summarize the result of the proposed method when applied to multiple vaccine-adverse event combinations.
We applied the proposed method to detect safety signals of FLU3, an influenza vaccine containing three flu strains, in the VAERS database. We showed that it had high statistical power to detect the variation in reporting rates across years. The identified vaccine-event combinations with significant different reporting rates over years suggested potential safety issues due to changes in vaccines which require further investigation.
We developed a statistical model to detect safety signals arising from heterogeneity of reporting rates of a given vaccine-event combinations across reporting years. This method detects variation in reporting rates over years with high power. The temporal trend of reporting rate across years may reveal the impact of vaccine update on occurrence of adverse events and provide evidence for further investigations.
通过手动审查大型监测数据库中的个体报告来识别安全信号非常耗时；这种方法不太可能揭示药物与不良事件之间的复杂关系。自 20 世纪 90 年代末以来，人们一直在努力开发数据挖掘工具，以便在监测数据库中系统地自动搜索安全信号。流感疫苗对安全性监测提出了特殊挑战，因为疫苗每年都会根据当年预测流行的流感菌株进行更新。因此，可以预期流感疫苗接种后不良事件（特定疫苗-事件组合的报告数/所有疫苗-事件组合的报告数）的报告率在报告年度之间会有很大差异。当前的监测方法很少考虑到信号检测中的这些变化，不同年份的报告通常合并在一起进行安全性分析。然而，合并来自不同年份的报告忽略了报告率在各年之间的潜在异质性，并且可能会错过重要的安全信号。
从疫苗不良事件报告系统（VAERS）数据库中提取了 1990 年至 2013 年期间的不良事件报告，并将其格式化为具有疫苗类型、不良事件组和报告时间作为三个维度的三维数据数组。我们提出了一个随机效应模型来测试给定疫苗-事件组合在报告年度之间的报告率的异质性。该方法提供了一种严格的统计程序，用于检测各年报告率之间的差异。我们还引入了一种新的可视化工具，用于总结应用于多个疫苗-不良事件组合时该方法的结果。
我们将提出的方法应用于 VAERS 数据库中含三种流感株的流感疫苗 FLU3 的安全性信号检测。结果表明，该方法具有很高的统计功效，可以检测各年报告率之间的变化。确定的在各年报告率存在显著差异的疫苗-事件组合提示由于疫苗变化而可能存在安全问题，需要进一步调查。
我们开发了一种统计模型，用于检测给定疫苗-事件组合在报告年度之间报告率的异质性引起的安全信号。该方法具有较高的功效，可以检测各年报告率的变化。各年报告率的时间趋势可以揭示疫苗更新对不良事件发生的影响，并为进一步调查提供证据。
