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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Vydáno v:Clinical pharmacology and therapeutics Ročník 93; číslo 6; s. 539
Hlavní autoři: Harpaz, R, DuMouchel, W, LePendu, P, Bauer-Mehren, A, Ryan, P, Shah, N H
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.06.2013
Témata:
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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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1532-6535
1532-6535
DOI:10.1038/clpt.2013.24