Automated signal generation in prescription-event monitoring
Automated signal generation in prescription-event monitoring
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DOI：
10.2165/00002018-200225060-00006
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发表时间：
2002-01-01
期刊：
影响因子：
4.2
通讯作者：
Shakir, SAW
中科院分区：
文献类型：
--
作者：
Heeley, E;Wilton, LV;Shakir, SAW
信号生成是一种突出药物中潜在安全问题的方法，然后需要进一步研究这些问题。以前自动信号生成主要应用于自发报告系统。药物安全研究单位 (DSRU) 使用称为处方事件监测 (PEM) 的方法对英国选定的新上市药物进行观察性上市后研究。 DSRU 研究了使用 PEM 研究中的事件数据生成信号的自动化程序。研究了比例报告比 (PRR) 和发生率比 (IRR)，作为 PEM 数据中信号生成的可能工具。 PEM 数据库包含 78 项已完成的初级保健药物研究，涉及各种治疗类别。我们进行了回顾性研究，以确定改变药物比较组的影响，同时分析 DSRU 分层字典中不同级别的结果，并在开始用药后 30 和 180 天的观察后执行信号生成。自动信号生成是一种有用的假设生成方法，很可能在临床试验和上市后研究中被证明是有用的。 PRR 的应用很简单，并且不需要分母。 IRR 考虑了受试者在感兴趣的事件之前接触药物的时间，并提供了有用且更深入的数据研究。然而，对于这两种方法，重要的是在字典中的多个级别上执行信号生成并仔细选择比较器组。
Signal generation is a method of highlighting potential safety issues in a drug that then need to be investigated further. Previously automated signal generation has mainly been applied to spontaneous reporting systems. The Drug Safety Research Unit (DSRU) per-forms observational postmarketing studies on selected newly marketed medicines in England using a method known as prescription-event monitoring (PEM). The DSRU has investigated automated procedures for the generation of signals using the event data from PEM studies.Proportional reporting ratios (PRRs) and incidence rate ratios (IRRs) were studied as possible tools for signal generation in PEM data. The PEM database contains 78 completed studies of drugs prescribed in primary care from a variety of therapeutic classes. Retrospective studies were carried out to identify the implications of changing the comparator group of drugs, along with analysing the results at different levels in the DSRUs hierarchical dictionary and performing signal generation after 30 and 180 days of observation since starting the drug.Automated signal generation is a useful hypothesis generating method that is likely to prove to be useful both in clinical trials and postmarketing studies. PRRs are simple to apply and do not require a denominator. IRRs take into account the time subjects were exposed to the drug prior to the event of interest, and offers a useful, and more in depth look into the data. However, with both methods it is important to per-form signal generation at multiple levels in the dictionary and with careful selection of the comparator group.