Likelihood Ratio Test-Based Method for Signal Detection in Drug Classes Using FDA's AERS Database
Likelihood Ratio Test-Based Method for Signal Detection in Drug Classes Using FDA's AERS Database
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DOI：
10.1080/10543406.2013.736810
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发表时间：
2013-01-01
影响因子：
1.1
通讯作者：
Tiwari, Ram C.
中科院分区：
文献类型：
--
作者：
Huang, Lan;Zalkikar, Jyoti;Tiwari, Ram C.
1968年，美国食品药品监督管理局（FDA）建立了不良事件报告系统（AERS）数据库，其中包含患者、医疗保健提供者和其他来源通过自发报告系统报告的不良事件（AE）数据。FDA使用AERS来监测批准后上市的药物的安全性。文献中用于分析大量上市后药物安全性数据以识别不成比例高频率药物事件组合的大多数统计方法旨在探索单一药物AE组合的信号，但不包括同时包含药物类别或一组AE的信号。这些方法也不是为了控制I类错误而设计的，并且会受到高错误发现率的影响。在本文中，我们首先简要回顾了最近开发的方法，被称为似然比检验（LRT）为基础的方法，已被证明可以控制家庭明智的I型错误和错误发现率。通过引入药物（或AE）的权重矩阵的概念，我们将LRT方法扩展到除了检测单个药物（或AE）的信号之外，还检测包括一类药物（或AE）的信号。提出了一种简化的贝叶斯方法，并与LRT方法进行了比较。所提出的方法被应用于研究药物类的信号模式，即，钆类药物的磁共振成像（MRI）和他汀类药物的高胆固醇血症，在不同的时间段内，使用的数据集，只有可疑药物和可疑药物和伴随药物从AERS数据库。通过统计方法检测到的信号可以通过在不同数据库中检测到的信号、来自研究或监管资源的现有医学证据、前瞻性生物学研究以及通过应用中所示的模拟来确认。
In 1968 the Food and Drug Administration (FDA) established the Adverse Event Reporting System (AERS) database containing data on adverse events (AEs) reported by patients, health care providers, and other sources through a spontaneous reporting system. FDA uses AERS for monitoring the safety of the drugs on the market after approval. Most statistical methods that are available in the literature to analyze large postmarket drug safety data for identifying drugevent combinations with disproportionately high frequencies are designed to explore signals of a single drugAE combination, but not signals including a drug class or a group of AEs simultaneously. Those methods are also not designed to control type I error and are subject to high false discovery rates. In this paper, we first briefly review a recently developed method, known as the likelihood ratio test (LRT)-based method, which has been demonstrated to control the family-wise type I error and false discovery rates. By introducing a concept of weight matrix for the drugs (or for AEs), we then extend the LRT method for detecting signals including a class of drugs (or AEs) in addition to detecting signals of single drug (or AE). A simplified Bayesian method is also proposed and compared with LRT method. The proposed methods are applied to study the signal patterns of drug classes, namely, the gadolinium drug class for magnetic resonance imaging (MRI) and statins for hypercholesterolemia, over different time periods, using the datasets with only suspect drugs and with both suspect and concomitant drugs from the AERS database. The signals detected by the statistical methods can be confirmed by signals detected across different databases, existing medical evidence from research or regulatory resources, prospective biological studies, and also through simulation as illustrated in the application.