| アブストラクト | PURPOSE: Spontaneous adverse event reporting systems are critical tools for monitoring the safety of licensed medical products. Commonly used signal detection algorithms identify disproportionate product-adverse event pairs and may not be sensitive to more complex potential signals. We sought to develop a computationally tractable multivariate data-mining approach to identify product-multiple adverse event associations. METHODS: We describe an application of stepwise association rule mining (Step-ARM) to detect potential vaccine-symptom group associations in the US Vaccine Adverse Event Reporting System. Step-ARM identifies strong associations between one vaccine and one or more adverse events. To reduce the number of redundant association rules found by Step-ARM, we also propose a clustering method for the post-processing of association rules. RESULTS: 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-ARM can detect a variety of medically coherent potential vaccine-symptom group signals efficiently. In the MMRV example, Step-ARM appears to outperform univariate methods in detecting a known safety signal. CONCLUSIONS: 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-processing clustering algorithm improves the applicability of the approach as a screening method to identify patterns that may merit further investigation. |
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| ジャーナル名 | Pharmacoepidemiology and drug safety |
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| 投稿日 | 2015/6/6 |
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| 投稿者 | Wei, Lai; Scott, John |
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| 組織名 | Division of Biostatistics, Office of Biostatistics and Epidemiology, Center for;Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver;Spring, MD, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/26045284/ |
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