| アブストラクト | Adverse drug event (ADE) is a significant challenge in clinical practice. Many ADEs have not been identified timely after the approval of the corresponding drugs. Despite the use of drug similarity network demonstrates early success on improving ADE detection, false discovery rate (FDR) control remains unclear in its application. Additionally, performance of early ADE detection has not been explicitly investigated under the time-to-event framework. In this manuscript, we propose to use the drug similarity based posterior probability of null hypothesis for early ADE detection. The proposed approach is also able to control FDR for monitoring a large number of ADEs of multiple drugs. The proposed approach outperforms existing approaches on mining labeled ADEs in the US FDA's Adverse Event Reporting System (FAERS) data, especially in the first few years after the drug initial reporting time. Additionally, the proposed approach is able to identify more labeled ADEs and has significantly lower time to ADE detection. In simulation study, the proposed approach demonstrates proper FDR control, as well as has better true positive rate and an excellent true negative rate. In our exemplified FAERS analysis, the proposed approach detects new ADE signals and identifies ADE signals in a timelier fashion than existing approach. In conclusion, the proposed approach is able to both reduce the time and improve the FDR control for ADE detection. |
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| ジャーナル名 | medRxiv : the preprint server for health sciences |
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| Pubmed追加日 | 2023/7/3 |
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| 投稿者 | Shi, Yi; Peng, Xueqiao; Liu, Ruoqi; Sun, Anna; Yang, Yuedi; Zhang, Ping; Zhang, Pengyue |
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| 組織名 | Department of Biostatistics and Health Data Science, Indiana University,;Indianapolis, Indiana, USA.;Department of Computer Science and Engineering, the Ohio State University,;Columbus, Ohio, USA.;Department of Biomedical Informatics, the Ohio State University, Columbus, Ohio,;USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/37398083/ |
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