Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial

被引:2748
作者
Salanti, Georgia [1 ]
Ades, A. E. [2 ]
Ioannidis, John P. A. [1 ,3 ,4 ,5 ]
机构
[1] Univ Ioannina, Sch Med, Dept Hyg & Epidemiol, GR-45110 Ioannina, Greece
[2] Univ Bristol, Dept Community Based Med, Bristol, Avon, England
[3] Tufts Univ, Dept Med, Tufts Med Ctr, Inst Clin Res & Hlth Policy Studies, Boston, MA 02111 USA
[4] Tufts Univ, Sch Med, Boston, MA 02111 USA
[5] Harvard Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
基金
欧洲研究理事会; 英国医学研究理事会;
关键词
Predictive intervals; Posterior probabilities; Ranking; Network meta-analysis; Mixed-treatment comparison; Bayesian meta-analysis; NETWORK METAANALYSIS; CLINICAL-TRIALS; INCONSISTENCY;
D O I
10.1016/j.jclinepi.2010.03.016
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:163 / 171
页数:9
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