论文标题
贝叶斯对Covid-19疫苗安全性的学习，同时结合不良事件本体
Bayesian learning of COVID-19 Vaccine safety while incorporating Adverse Events ontology
论文作者
论文摘要
尽管疫苗对于结束Covid-19的大流行至关重要，但公众对疫苗安全的信心一直很脆弱。许多统计方法已应用于VAER（疫苗不良事件报告系统）数据库，以研究COVID-19-19疫苗的安全性。但是，所有这些方法都忽略了不良事件（AE）本体论。 AE是自然相关的；例如，退缩，吞咽困难和反流的事件都与异常的消化系统有关。明确将AE关系带入模型可以帮助检测噪声中的真实AE信号，同时降低误报。我们提出了一个贝叶斯图形模型，以同时同时融合AE本体学估算所有AE。我们提出了构建共轭形式的策略，导致有效的吉布斯采样器。我们基于后验分布，提出了一种负面控制方法，以减轻报告偏见和一种富集方法来检测AE的关注群体。使用模拟研究评估了所提出的方法，并在研究Covid-19-19疫苗的安全性方面得到了进一步说明。提出的方法是在r pakage \ textit {bgrass}中实现的，源代码可在https://github.com/bangyaozhao/bgrass上获得。
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.
