Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials

TL;DR

This paper introduces a knowledge-based graphical method that enhances adverse event review in clinical trials by integrating semantic relationships and visual tools, improving signal detection accuracy and efficiency.

Contribution

It presents a novel knowledge layer added to MedDRA, enabling automatic clustering and better interpretation of adverse events in clinical trial data.

Findings

Successfully recovered all expected safety signals in legacy trials

Enhanced clarity and efficiency in adverse event interpretation

Improved accuracy of signal detection through semantic clustering

Abstract

We present a graphical, knowledge-based method for reviewing treatment-emergent adverse events (AEs) in clinical trials. The approach enhances MedDRA by adding a hidden medical knowledge layer (Safeterm) that captures semantic relationships between terms in a 2-D map. Using this layer, AE Preferred Terms can be regrouped automatically into similarity clusters, and their association to the trial disease may be quantified. The Safeterm map is available online and connected to aggregated AE incidence tables from ClinicalTrials.gov. For signal detection, we compute treatment-specific disproportionality metrics using shrinkage incidence ratios. Cluster-level EBGM values are then derived through precision-weighted aggregation. Two visual outputs support interpretation: a semantic map showing AE incidence and an expectedness-versus-disproportionality plot for rapid signal detection. Applied to three legacy trials, the automated method clearly recovers all expected safety signals. Overall, augmenting MedDRA with a medical knowledge layer improves clarity, efficiency, and accuracy in AE interpretation for clinical trials.