Mbuagbaw L, Rochwerg B, Jaeschke R, Heels-Andsell D, Alhazzani W, Thabane L, Guyatt Gordon H
Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada.
Biostatistics Unit, Father Sean O'Sullivan Research Centre, St Joseph's Healthcare, Hamilton, ON, Canada.
Syst Rev. 2017 Apr 12;6(1):79. doi: 10.1186/s13643-017-0473-z.
When randomized trials have addressed multiple interventions for the same health problem, network meta-analyses (NMAs) permit researchers to statistically pool data from individual studies including evidence from both direct and indirect comparisons. Grasping the significance of the results of NMAs may be very challenging. Authors may present the findings from such analyses in several numerical and graphical ways. In this paper, we discuss ranking strategies and visual depictions of rank, including the surface under the cumulative ranking (SUCRA) curve method. We present ranking approaches' merits and limitations and provide an example of how to apply the results of a NMA to clinical practice.
当随机试验针对同一健康问题涉及多种干预措施时，网状Meta分析（NMA）使研究人员能够对来自个体研究的数据进行统计学合并，包括直接和间接比较的证据。理解NMA结果的意义可能极具挑战性。作者可能会以多种数值和图形方式呈现此类分析的结果。在本文中，我们讨论了排名策略和排名的直观表示方法，包括累积排名曲线下面积（SUCRA）法。我们介绍了排名方法的优缺点，并提供了一个如何将NMA结果应用于临床实践的示例。
