{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T19:35:18Z","timestamp":1765827318960,"version":"3.37.3"},"reference-count":42,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2015,6,4]],"date-time":"2015-06-04T00:00:00Z","timestamp":1433376000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000038","name":"U.S. Food and Drug Administration","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmacoepidemiology and Drug"],"published-print":{"date-parts":[[2015,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product\u2013adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data\u2010mining approach to identify product\u2013multiple adverse event associations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We describe an application of stepwise association rule mining (Step\u2010ARM) to detect potential vaccine\u2010symptom group associations in the US Vaccine Adverse Event Reporting System. Step\u2010ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step\u2010ARM, we also propose a clustering method for the post\u2010processing of association rules.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In sample applications to a trivalent intradermal inactivated influenza virus vaccine and to measles, mumps, rubella, and varicella (MMRV) vaccine and in simulation studies, we find that Step\u2010ARM can detect a variety of medically coherent potential vaccine\u2010symptom group signals efficiently. In the MMRV example, Step\u2010ARM appears to outperform univariate methods in detecting a known safety signal.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our approach is sensitive to potentially complex signals, which may be particularly important when monitoring novel medical countermeasure products such as pandemic influenza vaccines. The post\u2010processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation. 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