Introduction Post-marketing drug surveillance is largely based on signals found in spontaneous reports from patients and healthcare providers. Rare adverse drug reactions and adverse events (AEs) that may develop after long-term exposure to a drug or from drug interactions may be missed. The US FDA and others have proposed that web-based data could be mined as a resource to detect latent signals associated with adverse drug reactions. Methods Recently, a web-based search query method called a query log reaction score (QLRS) was developed to detect whether AEs associated with certain drugs could be found from search engine query data. In this study, we compare the performance of two other algorithms, the proportional query ratio (PQR) and the proportional query
\nrate ratio (Q-PRR) against that of two reference signaldetection algorithms (SDAs) commonly used with the FDA AE Reporting System (FAERS) database. Results In summary, the web query methods have moderate sensitivity (80%) in detecting signals in web query data compared with reference SDAs in FAERS when the web query data are \ufb01ltered, but the query metrics generate many false-positives and have low speci\ufb01city compared with reference SDAs in FAERS. Conclusion Future research is needed to \ufb01nd better re\ufb01nements of query data and\/or the metrics to improve the speci\ufb01city of these web query log algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
Introduction Post-marketing drug surveillance is largely based on signals found in spontaneous reports from patients and healthcare providers. Rare adverse drug reactions and adverse events (AEs) that may develop after long-term exposure to a drug or from drug interactions may be missed. 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