The vaccine adverse event reporting system (VAERS) is a vital resource for post-licensure vaccine safety monitoring and has played a key role in assessing the safety of COVID-19 vaccines. However it is difficult to properly identify rare adverse events (AEs) associated with vaccines due to small or zero counts. We propose a Bayesian model with a Dirichlet Process Mixture prior to improve accuracy of the AE estimates with small counts by allowing data-guided information sharing between AE estimates. We also propose a negative control procedure embedded in our Bayesian model to mitigate the reporting bias due to the heightened awareness of COVID-19 vaccines, and use it to identify associated AEs as well as associated AE groups defined by the organ system in the Medical Dictionary for Regulatory Activities (MedDRA) ontology. The proposed model is evaluated using simulation studies, in which it outperforms baseline models without information sharing and is applied to study the safety of COVID-19 vaccines using VAERS data.

翻译：疫苗不良事件报告系统（VAERS）是疫苗上市后安全性监测的重要资源，在评估COVID-19疫苗安全性方面发挥了关键作用。然而，由于罕见不良事件（AE）的计数较小或为零，难以准确识别与疫苗相关的罕见不良事件。我们提出了一种基于狄利克雷过程混合先验的贝叶斯模型，通过允许不良事件估计之间的数据引导信息共享，提高了小计数情况下不良事件估计的准确性。我们还提出了一种嵌入贝叶斯模型中的阴性对照程序，以减轻因对COVID-19疫苗的高度关注而导致的报告偏倚，并利用该程序识别相关不良事件以及由《监管活动医学词典》（MedDRA）本体中器官系统定义的相关不良事件组。该模型通过模拟研究进行了评估，结果表明其优于无信息共享的基线模型，并已应用于利用VAERS数据研究COVID-19疫苗的安全性。