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Performance of Pharmacovigilance Signal‐Detection Algorithms for the FDA Adverse Event Reporting System
Author(s) -
Harpaz R,
DuMouchel W,
LePendu P,
BauerMehren A,
Ryan P,
Shah N H
Publication year - 2013
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2013.24
Subject(s) - pharmacovigilance , adverse event reporting system , observational study , food and drug administration , adverse effect , clinical pharmacology , computer science , confounding , event (particle physics) , postmarketing surveillance , medicine , patient safety , detection theory , masking (illustration) , risk analysis (engineering) , pharmacology , health care , economic growth , art , telecommunications , visual arts , physics , quantum mechanics , detector , economics
Signal‐detection algorithms (SDAs) are recognized as vital tools in pharmacovigilance. However, their performance characteristics are generally unknown. By leveraging a unique gold standard recently made public by the Observational Medical Outcomes Partnership (OMOP) and by conducting a unique systematic evaluation, we provide new insights into the diagnostic potential and characteristics of SDAs that are routinely applied to the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS). We find that SDAs can attain reasonable predictive accuracy in signaling adverse events. Two performance classes emerge, indicating that the class of approaches that address confounding and masking effects benefits safety surveillance. Our study shows that not all events are equally detectable, suggesting that specific events might be monitored more effectively using other data sources. We provide performance guidelines for several operating scenarios to inform the trade‐off between sensitivity and specificity for specific use cases. We also propose an approach and demonstrate its application in identifying optimal signaling thresholds, given specific misclassification tolerances. Clinical Pharmacology & Therapeutics (2013); 93 6, 539–546. doi: 10.1038/clpt.2013.24