利用不良事件的层次结构进行临床试验信号检测的统计方法:方法学文献综述。

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Laetitia de Abreu Nunes, Richard Hooper, Patricia McGettigan, Rachel Phillips
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引用次数: 0

摘要

背景:在具有疗效相关主要结果的随机对照试验中,收集不良事件是为了监测潜在的干预危害。由于数据的复杂性和大量未指定的结果,不良事件数据的分析具有挑战性。此外,由于缺乏最佳分析方法的指导,导致普遍存在操作不当和使用过于简单的方法的情况,从而使这些丰富的数据集得不到最佳利用。为了解决不良事件分析的复杂性,有人提出了利用数据中现有结构的统计方法,例如考虑根据生物或临床关系对不良事件进行分组:我们对文献进行了方法学范围审查,以确定所有利用数据结构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:结果: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)和误差控制程序(n=7),贝叶斯方法根据治疗效果的后验估计值标记事件,误差控制程序根据调整后的 p 值标记事件,同时控制某种类型的误差率。我们确定了 5 个界定方法学的特征:考虑的结果类型(如二元结果)、数据性质(如汇总数据)、分析时间(如最终分析)、对考虑事件的限制(如罕见事件)以及使用的分组系统:我们发现了大量使用不良事件分组结构的分析方法。该领域方法论的不断发展突出表明,人们日益认识到需要更好的做法。使用更充分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风险概况。统计学家可以利用本综述的结果来更好地了解当前的方法论状况,并确定合适的数据分析方法--尽管还需要进一步的研究来确定哪些方法最合适,并提出适当的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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