Zhao Bangyao, Zhong Yuan, Kang Jian, Zhao Lili
Department of Biostatistics, University of Michigan.
Ann Appl Stat. 2023 Dec;17(4):2887-2902. doi: 10.1214/23-aoas1743. Epub 2023 Oct 30.
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, none of these methods considered 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 graph-assisted signal selection (BGrass) model to simultaneously estimate all AEs while incorporating the network of dependence between AEs. Under a fully Bayesian inference framework, we also propose a negative control approach to mitigate the reporting bias and an enrichment approach to detecting AE groups of concern. For posterior computation we construct an equivalent model representation and develop an efficient Gibbs sampler. We evaluate the performance of BGrass via extensive simulations. To study the safety of COVID-19 vaccines, we apply BGrass to analyze approximately one million VAERS reports (01/01/2016-12/24/2021) involving more than 800 AEs. In particular, we found that blood clots (including deep vein thrombosis, thrombosis, and pulmonary embolism) are more likely to be reported after COVID-19 vaccination, compared to influenza vaccines. They are also reported more often for Johnson & Johnson-Janssen vaccine, compared to mRNA-based COVID-19 vaccines. A user-friendly R package BGrass that implements the proposed methods to assess vaccine safety is included in the Supplementary Material and is publicly available at https://github.com/BangyaoZhao/BGrass.
虽然疫苗对于终结新冠疫情至关重要，但公众对疫苗安全性的信心一直较为脆弱。许多统计方法已应用于疫苗不良事件报告系统（VAERS）数据库，以研究新冠疫苗的安全性。然而，这些方法均未考虑不良事件（AE）本体。不良事件之间存在天然关联；例如，干呕、吞咽困难和反流事件均与消化系统异常有关。明确将不良事件关系纳入模型有助于在噪声中检测真正的不良事件信号，同时减少假阳性。我们提出一种贝叶斯图辅助信号选择（BGrass）模型，在纳入不良事件之间的依赖网络的同时，对所有不良事件进行联合估计。在全贝叶斯推断框架下，我们还提出一种负对照方法来减轻报告偏差，以及一种富集方法来检测关注的不良事件组。对于后验计算，我们构建了一个等效的模型表示，并开发了一种高效的吉布斯采样器。我们通过广泛的模拟评估了BGrass的性能。为研究新冠疫苗的安全性，我们应用BGrass分析了约100万份VAERS报告（2016年1月1日至2021年12月24日），涉及800多种不良事件。特别是，我们发现与流感疫苗相比，接种新冠疫苗后更有可能报告血栓（包括深静脉血栓形成、血栓形成和肺栓塞）。与基于mRNA的新冠疫苗相比，强生-杨森疫苗报告血栓的情况也更频繁。补充材料中包含一个用户友好型的R包BGrass，它实现了所提出的评估疫苗安全性的方法，可在https://github.com/BangyaoZhao/BGrass上公开获取。
