Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial
Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial
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DOI：
10.1016/j.jclinepi.2010.03.016
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发表时间：
2011-02-01
影响因子：
7.2
通讯作者：
Ioannidis, John P. A.
中科院分区：
文献类型：
--
作者：
Salanti, Georgia;Ades, A. E.;Ioannidis, John P. A.
目的：提出一些简单的图形和定量方法，以帮助解释和改善多治疗荟萃分析（MTM）结果的呈现。研究设计和设置：我们重新分析了一个已发表的试验网络，该网络比较了各种抗血小板干预措施对严重血管事件发生率的影响，使用贝叶斯方法进行随机效应MTM，我们探讨了各种传统和新形式的定量显示和结果的图形表示的优点和缺点。我们以各种形式呈现结果，传统上基于效应量分布的平均值;基于预测;基于排序概率;最后，基于在参考的可接受范围内的概率。我们展示了如何获得和目前的结果排名的所有治疗和如何评估的整体ranks.Conclusions：贝叶斯方法提供了多种方式来呈现结果从MTM模型，因为它使一个自然和容易的估计所有措施的基础上的概率，排名，或预测。(C)2011 Elsevier Inc. All rights reserved.
Objective: To present some simple graphical and quantitative ways to assist interpretation and improve presentation of results from multiple-treatment meta-analysis (MTM).Study Design and Setting: We reanalyze a published network of trials comparing various antiplatelet interventions regarding the incidence of serious vascular events using Bayesian approaches for random effects MTM, and we explore the advantages and drawbacks of various traditional and new forms of quantitative displays and graphical presentations of results.Results: We present the results under various forms, conventionally based on the mean of the distribution of the effect sizes; based on predictions; based on ranking probabilities; and finally, based on probabilities to be within an acceptable range from a reference. We show how to obtain and present results on ranking of all treatments and how to appraise the overall ranks.Conclusions: Bayesian methodology offers a multitude of ways to present results from MTM models, as it enables a natural and easy estimation of all measures based on probabilities, ranks, or predictions. (C) 2011 Elsevier Inc. All rights reserved.