A Review of Statistical Methods for Spontaneous Reporting System Data Mining: Signal Detection and Beyond

TL;DR

This paper reviews statistical methods for analyzing spontaneous reporting system data in drug safety, focusing on signal detection, inference, and practical data preprocessing, with real-world opioid data examples.

Contribution

It provides a comprehensive review of contemporary SRS data mining approaches, including practical guidance on data preprocessing and illustrative opioid datasets.

Findings

Reviewed methods for signal detection and uncertainty quantification

Provided practical guidance on data preprocessing from aggregated counts

Illustrated analysis with opioid-related datasets from FAERS and VigiBase

Abstract

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.