Xu Zhiheng, Xu Jianjin, Yao Zhihao, Huang Lan, Jung Mary, Tiwari Ram
Center for Devices and Radiology Health, U.S. Food and Drug Administration, MD, USA.
J Biopharm Stat. 2021 Jan 2;31(1):37-46. doi: 10.1080/10543406.2020.1783284. Epub 2020 Jun 28.
Signal detection methods have been used extensively in post-market surveillance to identify elevated risks of adverse events. However, these statistical methods have not been widely used in detecting AE signals for medical devices. In this paper, we focused on the use of a likelihood ratio test (LRT)-based method in identifying adverse event (AE) signals associated with left ventricular assist devices (LVADs) using Medical Device Reporting (MDR) data. Among 110,927 adverse event entries identified in MDR data for LVADs, the LRT method detected 18 AE signals which included seven bleeding-related AEs such as hemolysis, thrombosis, hematuria, thrombus, blood loss, and hemorrhage. The LRT method was also applied to longitudinal data from 2007 to 2019 where a monotone alpha-spending function was used to ensure the control of type I error at each look and overall for trend analysis. Furthermore, the LRT method was compared to proportional reporting ratios (PRRs), Bayesian confidence propagation neural network (BCPNN), and simplified Bayes methods and found to be the most conservative method when examining the total number of detected signals, given its ability to control type-I error and the false discovery rate.
信号检测方法已广泛应用于上市后监测，以识别不良事件的风险升高情况。然而，这些统计方法在检测医疗器械的不良事件信号方面尚未得到广泛应用。在本文中，我们重点关注基于似然比检验（LRT）的方法，该方法利用医疗器械报告（MDR）数据识别与左心室辅助装置（LVAD）相关的不良事件（AE）信号。在LVAD的MDR数据中识别出的110,927条不良事件记录中，LRT方法检测到18个AE信号，其中包括7个与出血相关的不良事件，如溶血、血栓形成、血尿、血栓、失血和出血。LRT方法还应用于2007年至2019年的纵向数据，其中使用单调α花费函数来确保在每次观察时以及总体趋势分析中控制I型错误。此外，将LRT方法与比例报告比（PRR）、贝叶斯置信传播神经网络（BCPNN）和简化贝叶斯方法进行比较，发现鉴于其控制I型错误和错误发现率的能力，在检查检测到的信号总数时，LRT方法是最保守的方法。
