Xu Zhiheng, Kass-Hout Taha, Anderson-Smits Colin, Gray Gerry
Division of Biostatistics, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Chief Health Informatics Officer, Chief Technology Officer, Office of Informatics and Technology Innovation, Office of Operations, Office of the Commissioner, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Pharmacoepidemiol Drug Saf. 2015 Jun;24(6):663-8. doi: 10.1002/pds.3783. Epub 2015 Apr 22.
Signal detection methods have been used extensively in postmarket surveillance to identify elevated risks of adverse events associated with medical products (drugs, vaccines, and devices). However, current popular disproportionality methods ignore useful information such as trends when the data are aggregated over time for signal detection.
In this paper, we applied change point analysis (CPA) to trend analysis of medical products in a spontaneous adverse event reporting system. CPA was used to detect the time point at which statistical properties of a sequence of observations change over time. Two CPA approaches, change in mean and change in variance, were demonstrated by an example using neurostimulator adverse event dataset.
Two significant change points associated with upward trends were detected in June 2008 (n = 20, p < 0.001) and May 2011 (n = 51, p = 0.003). Further investigation confirmed battery issues and expansion of the indication for use could be possible causes for the occurrence of these change points. Two time points showed extremely low number of loss of therapy events, two cases in October 2009 and three in November 2009, which could be the result of reporting issues such as underreporting.
As a complimentary tool to current signal detection efforts at FDA, CPA can be used to detect changes in the association between medical products and adverse events over time. Detecting these changes could be critical for public health regulation, adverse events surveillance, product recalls, and regulators' understanding of the connection between adverse events and other events regarding regulated products. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.
信号检测方法已广泛应用于上市后监测，以识别与医疗产品（药物、疫苗和器械）相关的不良事件的风险升高情况。然而，当前流行的不成比例方法在为信号检测对数据进行时间汇总时忽略了诸如趋势等有用信息。
在本文中，我们将变化点分析（CPA）应用于自发不良事件报告系统中医疗产品的趋势分析。CPA用于检测观测序列的统计特性随时间变化的时间点。通过使用神经刺激器不良事件数据集的示例展示了两种CPA方法，即均值变化和方差变化。
在2008年6月（n = 20，p < 0.001）和2011年5月（n = 51，p = 0.003）检测到两个与上升趋势相关的显著变化点。进一步调查证实电池问题和适应症扩展可能是这些变化点出现的原因。两个时间点显示治疗失败事件数量极低，2009年10月有2例，2009年11月有3例，这可能是报告问题（如报告不足）导致的。
作为FDA当前信号检测工作的补充工具，CPA可用于检测医疗产品与不良事件之间随时间的关联变化。检测这些变化对于公共卫生监管、不良事件监测、产品召回以及监管机构对不良事件与其他有关受监管产品事件之间联系的理解可能至关重要。© 2015作者。由John Wiley & Sons, Ltd出版的《药物流行病学与药物安全》
