What question did this study set out to answer?

To review quantitative data mining algorithms used for adverse event signal detection in the FDA Adverse Event Reporting System (FAERS) database.

January 1, 2013International Journal of Medical SciencesOpen Access

Data Mining of the Public Version of the FDA Adverse Event Reporting System

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To review quantitative data mining algorithms used for adverse event signal detection in the FDA Adverse Event Reporting System (FAERS) database.
Evaluated four disproportionality data mining algorithms: proportional reporting ratio (PRR), reporting odds ratio (ROR), information component (IC), and empirical Bayes geometric mean (EBGM).
Compared signal yield and hierarchical overlap across algorithms using post-marketing safety data for warfarin, aspirin, and clopidogrel.
ROR detected the highest number of adverse event signals, whereas EBGM detected the lowest number of signals across datasets.
All EBGM-derived signals were completely nested within PRR-, IC-, and ROR-derived signals, and both PRR- and IC-based signals were captured within ROR-based signals.

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Implication

Pharmacovigilance review reveals varying signal detection sensitivity across data mining algorithms in FAERS safety reports, highlighting trade-offs for pharmacoepidemiological surveillance.

Cite This Study

Sakaeda et al. (2013) studied this question.

synapsesocial.com/papers/69d72dc13f906f6a06bef9e6 https://doi.org/10.7150/ijms.6048

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Implication

Pharmacovigilance review reveals varying signal detection sensitivity across data mining algorithms in FAERS safety reports, highlighting trade-offs for pharmacoepidemiological surveillance.

Data Mining of the Public Version of the FDA Adverse Event Reporting System

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Discussion

Implication

Cite This Study

Data Mining of the Public Version of the FDA Adverse Event Reporting System

Key Points

Discussion

Implication

Cite This Study

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