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Systematic Investigation of Time Windows for Adverse Event Data Mining for Recently Approved Drugs

From ProSanos Corporation, Harrisburg, Pennsylvania (Mr Hochberg, Dr Pearson, Mr O'Hara, Ms Reisinger); Pfizer Corporation, New York (Dr Hauben); New York University School of Medicine, New York (Dr Hauben); New York Medical College, Valhalla, New York (Dr Hauben); and Brunel University, West London, UK (Dr Hauben).

The optimum timing of drug safety data mining for a new drug is uncertain. The objective of this study was to compare cumulative data mining versus mining with sliding time windows. Adverse Event Reporting System data (2001-2005) were studied for 27 drugs. A literature database was used to evaluate signals of disproportionate reporting (SDRs) from an urn model data-mining algorithm. Data mining was applied cumulatively and with sliding time windows from 1 to 4 years in width. Time from SDR generation to the appearance of a publication describing the corresponding adverse event was calculated. Cumulative data mining and 1- to 2-year sliding windows produced the most SDRs for recently approved drugs. In the first postmarketing year, data mining produced SDRs an average of 800 days in advance of publications regarding the corresponding drug-event combination. However, this timing advantage reduced to zero by year 4. The optimum window width for sliding windows should increase with time on the market. Data mining may be most useful for early signal detection during the first 3 years of a drug's postmarketing life. Beyond that, it may be most useful for supporting or weakening hypotheses.

Key Words: Pharmacovigilance • data mining • time to signal

Address for reprints: Alan Hochberg, ProSanos Corporation, 225 Market St, Suite 502, Harrisburg, PA 17102; e-mail: alan.hochberg{at}prosanos.com.

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