Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events
François Haguinet, Jeffery L Painter, Gregory E Powell, Andrea Callegaro, Andrew Bate
TL;DR
This study introduces a semantic similarity–informed Bayesian borrowing (IC SSM) framework to enhance quantitative signal detection in spontaneous reporting systems. By embedding a robust MAP prior and weighting information from semantically similar MedDRA PTs, IC SSM achieves higher sensitivity and earlier detection than traditional IC and HLGT-based borrowing while maintaining stable performance in early post-marketing periods. Using FAERS data and a time-stamped PVLens reference set, the authors demonstrate improved detection of true positives—often months earlier—across multiple product–event pairs, with a transparent sensitivity to key prior and similarity parameters. The approach offers a scalable, context-aware enhancement to disproportionality analysis and holds promise for validation across other data sources and similarity metrics, potentially improving timeliness and accuracy of pharmacovigilance signals.
Abstract
We present a Bayesian dynamic borrowing (BDB) approach to enhance the quantitative identification of adverse events (AEs) in spontaneous reporting systems (SRSs). The method embeds a robust meta-analytic predictive (MAP) prior with a Bayesian hierarchical model and incorporates semantic similarity measures (SSMs) to enable weighted information sharing from clinically similar MedDRA Preferred Terms (PTs) to the target PT. This continuous similarity-based borrowing overcomes limitations of rigid hierarchical grouping in current disproportionality analysis (DPA). Using data from the FDA Adverse Event Reporting System (FAERS) between 2015 and 2019, we evaluate our approach -- termed IC SSM -- against traditional Information Component (IC) analysis and IC with borrowing at the MedDRA high-level group term level (IC HLGT). A reference set (PVLens), derived from FDA product label update, enabled prospective evaluation of method performance in identifying AEs prior to official labeling. The IC SSM approach demonstrated higher sensitivity (1332/2337=0.570, Youden's J=0.246) than traditional IC (Se=0.501, J=0.250) and IC HLGT (Se=0.556, J=0.225), consistently identifying more true positives and doing so on average 5 months sooner than traditional IC. Despite a marginally lower aggregate F1-score and Youden's index, IC SSM showed higher performance in early post-marketing periods or when the detection threshold was raised, providing more stable and relevant alerts than IC HLGT and traditional IC. These findings support the use of SSM-informed Bayesian borrowing as a scalable and context-aware enhancement to traditional DPA methods, with potential for validation across other datasets and exploration of additional similarity metrics and Bayesian strategies using case-level data.