以心理健康为重点的网络荟萃分析入门。
A primer on network meta-analysis with emphasis on mental health.
作者信息
Mavridis Dimitris, Giannatsi Myrsini, Cipriani Andrea, Salanti Georgia
机构信息
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Primary Education, University of Ioannina, Ioannina, Greece;
Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece;
出版信息
Evid Based Ment Health. 2015 May;18(2):40-6. doi: 10.1136/eb-2015-102088.
OBJECTIVE
A quantitative synthesis of evidence via standard pair-wise meta-analysis lies on the top of the hierarchy for evaluating the relative effectiveness or safety between two interventions. In most healthcare problems, however, there is a plethora of competing interventions. Network meta-analysis allows to rank competing interventions and evaluate their relative effectiveness even if they have not been compared in an individual trial. The aim of this paper is to explain and discuss the main features of this statistical technique.
METHODS
We present the key assumptions underlying network meta-analysis and the graphical methods to visualise results and information in the network. We used one illustrative example that compared the relative effectiveness of 15 antimanic drugs and placebo in acute mania.
RESULTS
A network plot allows to visualise how information flows in the network and reveals important information about network geometry. Discrepancies between direct and indirect evidence can be detected using inconsistency plots. Relative effectiveness or safety of competing interventions can be presented in a league table. A contribution plot reveals the contribution of each direct comparison to each network estimate. A comparison-adjusted funnel plot is an extension of simple funnel plot to network meta-analysis. A rank probability matrix can be estimated to present the probabilities of all interventions assuming each rank and can be represented using rankograms and cumulative probability plots.
CONCLUSIONS
Network meta-analysis is very helpful in comparing the relative effectiveness and acceptability of competing treatments. Several issues, however, still need to be addressed when conducting a network meta-analysis for the results to be valid and correctly interpreted.
目的
通过标准的成对荟萃分析对证据进行定量综合，处于评估两种干预措施之间相对有效性或安全性的证据等级体系的顶端。然而，在大多数医疗保健问题中，存在大量相互竞争的干预措施。网状荟萃分析能够对相互竞争的干预措施进行排序，并评估它们的相对有效性，即使它们未在个体试验中进行比较。本文旨在解释和讨论这种统计技术的主要特征。
方法
我们介绍了网状荟萃分析的关键假设以及用于可视化网络中结果和信息的图形方法。我们使用了一个示例，比较了15种抗躁狂药物和安慰剂在急性躁狂症中的相对有效性。
结果
网络图能够可视化信息在网络中的流动方式，并揭示有关网络几何结构的重要信息。使用不一致性图可以检测直接证据和间接证据之间的差异。相互竞争的干预措施的相对有效性或安全性可以在排名表中呈现。贡献图揭示了每个直接比较对每个网络估计值的贡献。比较调整漏斗图是简单漏斗图向网状荟萃分析的扩展。可以估计排名概率矩阵，以呈现所有干预措施假定每个排名的概率，并可以使用排名图和累积概率图来表示。
结论
网状荟萃分析在比较相互竞争的治疗方法的相对有效性和可接受性方面非常有帮助。然而，在进行网状荟萃分析时，仍有几个问题需要解决，以使结果有效并得到正确解释。
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