Shi Yi, Peng Xueqiao, Liu Ruoqi, Sun Anna, Yang Yuedi, Zhang Ping, Zhang Pengyue
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.
medRxiv. 2023 Jun 4:2023.05.31.23290792. doi: 10.1101/2023.05.31.23290792.
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.
药物不良事件（ADE）是临床实践中的一项重大挑战。许多ADE在相应药物获批后并未得到及时识别。尽管药物相似性网络的应用在改善ADE检测方面初显成效，但其应用中的错误发现率（FDR）控制仍不明确。此外，在事件发生时间框架下，早期ADE检测的性能尚未得到明确研究。在本论文中，我们建议使用基于药物相似性的零假设后验概率进行早期ADE检测。所提出的方法还能够控制FDR，以监测多种药物的大量ADE。在所提出的方法在挖掘美国食品药品监督管理局不良事件报告系统（FAERS）数据中的标记ADE方面优于现有方法，尤其是在药物首次报告时间后的头几年。此外，所提出的方法能够识别更多标记ADE，且ADE检测时间显著更短。在模拟研究中，所提出的方法展示了适当的FDR控制，以及更好的真阳性率和出色的真阴性率。在我们示例性的FAERS分析中，所提出的方法比现有方法能更及时地检测新的ADE信号并识别ADE信号。总之，所提出的方法能够在减少ADE检测时间的同时改善FDR控制。
