School of Public Health and Preventive Medicine, Monash University, level 1, 549 St Kilda Road, Melbourne, Victoria, Australia.
Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
Syst Rev. 2017 Jun 24;6(1):119. doi: 10.1186/s13643-017-0511-x.
Network meta-analysis, a method to synthesise evidence from multiple treatments, has increased in popularity in the past decade. Two broad approaches are available to synthesise data across networks, namely, arm- and contrast-synthesis models, with a range of models that can be fitted within each. There has been recent debate about the validity of the arm-synthesis models, but to date, there has been limited empirical evaluation comparing results using the methods applied to a large number of networks. We aim to address this gap through the re-analysis of a large cohort of published networks of interventions using a range of network meta-analysis methods.
We will include a subset of networks from a database of network meta-analyses of randomised trials that have been identified and curated from the published literature. The subset of networks will include those where the primary outcome is binary, the number of events and participants are reported for each direct comparison, and there is no evidence of inconsistency in the network. We will re-analyse the networks using three contrast-synthesis methods and two arm-synthesis methods. We will compare the estimated treatment effects, their standard errors, treatment hierarchy based on the surface under the cumulative ranking (SUCRA) curve, the SUCRA value, and the between-trial heterogeneity variance across the network meta-analysis methods. We will investigate whether differences in the results are affected by network characteristics and baseline risk.
The results of this study will inform whether, in practice, the choice of network meta-analysis method matters, and if it does, in what situations differences in the results between methods might arise. The results from this research might also inform future simulation studies.
网络荟萃分析是一种综合多种治疗方法证据的方法，在过去十年中越来越受欢迎。有两种广泛的方法可用于跨网络综合数据，即臂合成模型和对比合成模型，每种方法都有一系列可以拟合的模型。最近关于臂合成模型有效性的争论不断，但迄今为止，对于应用于大量网络的方法的结果比较，还没有进行有限的实证评估。我们旨在通过应用一系列网络荟萃分析方法重新分析一组大量已发表的干预措施网络来解决这一差距。
我们将包括从已发表文献中确定和整理的随机试验网络荟萃分析数据库中的网络子集。网络子集将包括那些主要结局为二分类、每个直接比较都报告了事件和参与者数量且网络中没有不一致证据的网络。我们将使用三种对比合成方法和两种臂合成方法重新分析这些网络。我们将比较估计的治疗效果、它们的标准误差、基于累积排序曲线下面积（SUCRA）的治疗层次结构、SUCRA 值以及网络荟萃分析方法之间的试验间异质性方差。我们将调查结果差异是否受网络特征和基线风险的影响。
本研究的结果将说明在实践中，网络荟萃分析方法的选择是否重要，如果是，方法之间结果的差异在什么情况下可能出现。这项研究的结果也可能为未来的模拟研究提供信息。
