Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node-splitting analyses are labour-intensive because each comparison of interest requires a separate model. It would be advantageous if node-splitting models could be estimated automatically for all comparisons of interest. We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi-arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node-splitting approach, enabling the analyst to focus on interpreting the results. (C) 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.
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机译：通过网络荟萃分析，可以同时合成比较多种治疗方法的临床试验网络。相对治疗效果的估计之间潜在的不一致是一个重要问题，已经提出了几种检测不一致的方法。本文关注的是节点拆分方法，由于它的直接解释，直接和间接证据的对比，该方法特别有吸引力。但是，节点拆分分析是劳动密集型的，因为每个感兴趣的比较都需要一个单独的模型。如果可以针对所有感兴趣的比较自动估计节点分裂模型，将是有利的。我们提出了一个明确的决策规则来选择要拆分的比较，并证明它仅选择网络中潜在不一致的环路中的比较，并且调查了网络中所有潜在不一致的环路。此外，决策规则规避了多臂试验的参数化问题，从而确保在所有情况下模型生成都是微不足道的。因此，我们的方法消除了使用节点拆分方法所涉及的大部分手动工作，从而使分析人员能够专注于解释结果。 （C）2015 The Authors Research Synthesis Methods，由John Wiley＆Sons Ltd.出版
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