Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.
Drug Saf. 2012 Jan 1;35(1):61-78. doi: 10.2165/11596630-000000000-00000.
The detection of adverse events following immunization (AEFI) fundamentally depends on how these events are classified. Standard methods impose a choice between either grouping similar events together to gain power or splitting them into more specific definitions. We demonstrate a method of medically guided Bayesian information sharing that avoids grouping or splitting the data, and we further combine this with the standard epidemiological tools of stratification and multivariate regression.
The aim of this study was to assess the ability of a Bayesian hierarchical model to identify gastrointestinal AEFI in children, and then combine this with testing for effect modification and adjustments for confounding.
Reporting odds ratios were calculated for each gastrointestinal AEFI and vaccine combination. After testing for effect modification, these were then re-estimated using multivariable logistic regression adjusting for age, sex, year and country of report. A medically guided hierarchy of AEFI terms was then derived to allow information sharing in a Bayesian model.
All spontaneous reports of AEFI in children under 18 years of age in the WHO VigiBase™ (Uppsala Monitoring Centre, Uppsala, Sweden) before June 2010. Reports with missing age were included in the main analysis in a separate category and excluded in a subsequent sensitivity analysis.
The 15 most commonly prescribed childhood vaccinations, excluding influenza vaccines.
All gastrointestinal AEFI coded by WHO Adverse Reaction Terminology.
A crude analysis identified 132 signals from 655 reported combinations of gastrointestinal AEFI. Adjusting for confounding by age, sex, year of report and country of report, where appropriate, reduced the number of signals identified to 88. The addition of a Bayesian hierarchical model identified four further signals and removed three. Effect modification by age and sex was identified for six vaccines for the outcomes of vomiting, nausea, diarrhoea and salivary gland enlargement.
This study demonstrated a sequence of methods for routinely analysing spontaneous report databases that was easily understandable and reproducible. The combination of classical and Bayesian methods in this study help to focus the limited resources for hypothesis testing studies towards adverse events with the strongest support from the data.
疫苗不良事件（AEFI）的检测从根本上取决于如何对这些事件进行分类。标准方法在将相似事件分组以获得效力或将其细分为更具体的定义之间做出选择。我们展示了一种医学指导的贝叶斯信息共享方法，该方法避免了数据的分组或细分，并且我们进一步将其与分层和多变量回归的标准流行病学工具相结合。
本研究旨在评估贝叶斯分层模型识别儿童胃肠道 AEFI 的能力，然后结合检验效应修饰和混杂因素调整。
为每个胃肠道 AEFI 和疫苗组合计算报告比值比。在检验了效应修饰后，使用多变量逻辑回归对其进行重新估计，调整了年龄、性别、报告年份和报告国家。然后推导出一个医学指导的 AEFI 术语层次结构，以允许在贝叶斯模型中进行信息共享。
2010 年 6 月前，WHO VigiBase（乌普萨拉监测中心，乌普萨拉，瑞典）中 18 岁以下儿童所有自发报告的 AEFI。报告中缺失年龄的报告被包含在主要分析中一个单独的类别中，并在随后的敏感性分析中被排除。
15 种最常用的儿童疫苗，不包括流感疫苗。
所有由世界卫生组织不良反应术语编码的胃肠道 AEFI。
对 655 种胃肠道 AEFI 报告组合进行了一项粗略分析，确定了 132 个信号。在适当情况下，通过年龄、性别、报告年份和报告国家的混杂因素调整后，识别出的信号数量减少到 88 个。贝叶斯分层模型的添加确定了另外 4 个信号，并消除了 3 个信号。年龄和性别对六种疫苗的呕吐、恶心、腹泻和唾液腺肿大的结局存在效应修饰。
本研究展示了一种用于常规分析自发报告数据库的方法序列，该方法易于理解和重现。本研究中经典和贝叶斯方法的结合有助于将有限的资源用于针对数据提供最强支持的不良事件的假设检验研究。
