Postmarketing safety surveillance relies on data from spontaneous reporting systems (SRS) such as FAERS, EudraVigilance and VigiBase, and commonly uses SRS data mining methods to assess the associations between drugs and adverse events (AEs). Traditionally, these analyses have focused on signal detection framed as a binary decision problem, whereas more recent work has emphasized more nuanced inference involving signal strength estimation and uncertainty quantification. In this paper, we review contemporary SRS data mining approaches and their statistical underpinnings for safety assessment using data from major pharmacovigilance databases worldwide. In addition to methodological review, we provide practical guidance on data preprocessing for such analysis, including construction of SRS contingency tables using only aggregated AE-drug counts, as are publicly available from databases such as VigiBase and EudraVigilance. We illustrate the guidance via opioid-related datasets obtained from FAERS and VigiBase, complied with subsequent downstream SRS data analyses.

翻译：上市后安全性监测依赖于自发呈报系统（SRS）数据，例如FAERS、EudraVigilance和VigiBase，并通常采用SRS数据挖掘方法评估药物与不良事件（AE）之间的关联。传统上，这些分析聚焦于作为二分类决策问题的信号检测，而近年来的研究更强调涉及信号强度估计和不确定性量化的精细化推断。本文综述了当代SRS数据挖掘方法及其在利用全球主要药物警戒数据库数据进行安全性评估时的统计基础。除方法学综述外，我们还为此类分析中的数据预处理提供实用指导，包括仅使用从VigiBase和EudraVigilance等数据库公开获取的聚合AE-药物计数来构建SRS列联表。我们通过从FAERS和VigiBase获取的阿片类药物相关数据集（这些数据集已整理用于后续下游SRS数据分析）来阐释该指导原则。