As a national public health surveillance resource, Vaccine Adverse Event Reporting System (VAERS) is a key component in ensuring the safety of vaccines. Numerous methods have been used to conduct safety studies with the VAERS database. These efforts focus on the downstream statistical analysis of the vaccine and adverse event associations. In this article, we primarily focus on processing the raw data in VAERS before the analysis step, which is also an important part of the signal detection process. Due to the semiannual update in the Medical Dictionary for Regulatory Activities (MedDRA) coding system, adverse event terms that describe the same symptom might change in VAERS; therefore, we identify these terms and combine them to increase the signal detection power. We also consider the uncertainty of the vaccine and adverse event pairs that arise from reports with multiple vaccines. Finally, we discuss four commonly used statistics in assessing the vaccine and adverse event associations, and propose to use the statistics that are robust to the reporting bias in VAERS and adjust for potential confounders of the vaccine and adverse event association to increase signal detection accuracy.

作为国家公共卫生监测资源，疫苗不良事件报告系统（VAERS）是确保疫苗安全的关键组成部分。 VAERS 数据库已使用多种方法进行安全性研究。这些工作的重点是疫苗和不良事件关联的下游统计分析。在本文中，我们主要关注在分析步骤之前处理 VAERS 中的原始数据，这也是信号检测过程的重要组成部分。由于监管活动医学词典 (MedDRA) 编码系统每半年更新一次，描述相同症状的不良事件术语可能会在 VAERS 中发生变化；因此，我们识别这些术语并将它们组合起来以提高信号检测能力。我们还考虑了疫苗的不确定性以及多种疫苗报告中出现的不良事件对。最后，我们讨论了评估疫苗和不良事件关联的四种常用统计数据，并建议使用对 VAERS 报告偏倚稳健的统计数据，并调整疫苗和不良事件关联的潜在混杂因素，以提高信号检测准确性。