While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, all these methods ignored the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graphical model to estimate all AEs while incorporating the AE ontology simultaneously. We proposed strategies to construct conjugate forms leading to an efficient Gibbs sampler. Built upon the posterior distributions, we proposed a negative control approach to mitigate reporting bias and an enrichment approach to detect AE groups of concern. The proposed methods were evaluated using simulation studies and were further illustrated on studying the safety of COVID-19 vaccines. The proposed methods were implemented in R package \textit{BGrass} and source code are available at https://github.com/BangyaoZhao/BGrass.
翻译:虽然疫苗对于结束COVID-19大流行至关重要,但公众对疫苗安全的信心始终很脆弱,许多统计方法已应用于VAERS数据库(VAERS(Vaccine Aview Astitution Report System)),以研究COVID-19疫苗的安全性,然而,所有这些方法都忽视了有害事件的肿瘤学。AEs自然具有关联性;例如,重烧、麻痹和回流事件都与异常消化系统有关。将AE关系明确纳入模型有助于在噪音中探测真正的AE信号,同时减少假阳性。我们提议了一个Bayesian图形模型模型来估计所有AE(VAVID-19)疫苗的安全性,同时纳入AE肿瘤学。我们提议了一些战略来构建同源表,导致高效的Gibs采样器。我们提议了一种负面控制方法,以减少报告偏差和浓缩方法,以探测AEE关切的人群。建议的方法通过模拟研究得到评估,并在研究COVID-19疫苗的安全性信号时得到进一步的说明。我们提议的方法是在ABGRA/BGRA Bro/Brops Brodrodrodrops。