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

2026-04-20

AI Summary

This study addresses the limitations of signal detection for drug–adverse event associations in spontaneous reporting systems (SRS) by moving beyond conventional binary decision frameworks. It proposes an integrated analytical approach that jointly estimates signal strength and quantifies associated uncertainty. Drawing on a systematic review of contingency table modeling and both Bayesian and frequentist signal detection methodologies, the work establishes a standardized preprocessing and analysis pipeline tailored to aggregated count data from major pharmacovigilance databases such as FAERS and VigiBase. Empirical evaluation using opioids as a case study demonstrates that the proposed method offers practical utility, reproducibility, and statistical rigor, thereby providing more nuanced decision support for pharmacovigilance activities.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Multiagent Systems: Adversarial Agents
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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 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.

Problem

• spontaneous reporting system
• signal detection
• adverse event
• pharmacovigilance
• drug safety

Innovation

• signal detection
• spontaneous reporting system
• uncertainty quantification
• signal strength estimation
• pharmacovigilance