| アブストラクト | The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System. |
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| ジャーナル名 | Biostatistics (Oxford, England) |
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| Pubmed追加日 | 2023/10/27 |
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| 投稿者 | Heo, Seok-Jae; Jeong, Sohee; Jung, Dagyeom; Jung, Inkyung |
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| 組織名 | Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei;University College of Medicine, Seoul 03722, Korea. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/37886808/ |
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