Knowledge-based Graphical Method for Safety Signal Detection in Clinical Trials
This addresses the need for improved clarity, efficiency, and accuracy in adverse event interpretation for clinical trial researchers, but it is incremental as it builds on existing MedDRA standards.
The authors tackled the problem of detecting safety signals from adverse events in clinical trials by developing a graphical, knowledge-based method that enhances MedDRA with a semantic layer, and the result was that it clearly recovered all expected safety signals in three legacy trials.
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.