Wolfson Institute of Population Health, Queen Mary University of London, London, UK.
William Harvey Research Institute, Queen Mary University of London, London, UK.
BMC Med Res Methodol. 2024 Oct 28;24(1):253. doi: 10.1186/s12874-024-02369-1.
In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.
We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.
We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.
We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.
在与疗效相关的主要结局的随机对照试验中，收集不良事件以监测潜在的干预危害。由于数据的复杂性和未指定的结果数量众多，不良事件数据的分析具有挑战性。由于缺乏最佳分析方法的指导，这一问题更加复杂，导致广泛存在分析方法不充分的情况，以及过度简单化方法的使用；导致这些丰富数据集没有得到最佳利用。为了解决不良事件分析的复杂性，提出了利用数据中现有结构的统计方法，例如根据不良事件的生物学或临床关系进行分组。
我们对文献进行了方法学范围的综述，以确定所有使用数据中结构来检测试验中不良反应信号的现有方法。我们系统地检索了 Embase、MEDLINE、Scopus 和 Web of Science 数据库。我们审查了每种方法的分析方法，提取了方法学特征，并对研究结果进行了叙述性总结。
我们从 14 个来源中确定了 18 种不同的方法。这些方法分为贝叶斯方法（n=11）和错误控制程序（n=7），前者根据治疗效果的后验估计标记事件，后者根据调整后的 p 值并控制某种类型的错误率来标记事件。我们确定了 5 个定义性方法特征：所考虑的结局类型（例如二分类结局）、数据性质（例如汇总数据）、分析时间（例如最终分析）、所考虑事件的限制（例如罕见事件）和使用的分组系统。
我们发现了大量使用不良事件组结构的分析方法。该领域的连续方法进展突出表明，需要更好的实践。使用更适当的分析方法可以帮助试验人员更好地了解干预措施的安全性风险概况。本综述的结果可被统计学家用于更好地了解当前的方法学现状，并确定适合数据分析的方法——尽管需要进一步研究来确定哪些方法最适合并制定适当的建议。
