Novel Data-Mining Methodologies for Adverse Drug Event Discovery and Analysis¶
Harpaz, R., DuMouchel, W., Shah, N. H., Madigan, D., Ryan, P., & Friedman, C. (2012). Novel Data-Mining Methodologies for Adverse Drug Event Discovery and Analysis. Clinical Pharmacology & Therapeutics, 91(6), 1010-1021.
Cited by¶
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Primes¶
- Parallel Independent Inspection
- In drug safety, post-market pharmacovigilance aggregates adverse-event reports from many physicians to surface signals invisible in any single trial.
This sourcePost-market pharmacovigilance aggregates spontaneous adverse-event reports from many physicians (e.g., FAERS) and applies disproportionality/signal-detection across enormous case volume to surface rare drug effects invisible in any single pre-market trial.
SupportedVerified against the work's full text
Harpaz et al. describe post-approval surveillance pooling spontaneous adverse-event reports from clinicians and others and mining them for signals, because trials cannot reveal the full safety profile.
“Drug safety surveillance has predominantly relied on spontaneous reporting systems (SRS), which are passive systems comprised of reports of suspected ADEs collected from healthcare professionals, consumers, and pharmaceutical companies”
- In drug safety, post-market pharmacovigilance aggregates adverse-event reports from many physicians to surface signals invisible in any single trial.
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