Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system

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 syste...

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Veröffentlicht in:Clinical pharmacology and therapeutics Jg. 93; H. 6; S. 539
Hauptverfasser: Harpaz, R, DuMouchel, W, LePendu, P, Bauer-Mehren, A, Ryan, P, Shah, N H
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.06.2013
Schlagworte:
Adverse Drug Reaction Reporting Systems - statistics & numerical data
Algorithms
Humans
Models, Statistical
Pharmacovigilance
United States
United States Food and Drug Administration
United States
ISSN:1532-6535, 1532-6535
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1532-6535
1532-6535
DOI:10.1038/clpt.2013.24