MRC Clinical Trials Unit at University College London, London, UK.
Advanced Research Computing, University College London (UCL), London, UK.
Stat Med. 2023 Nov 30;42(27):4917-4930. doi: 10.1002/sim.9872. Epub 2023 Sep 28.
In network meta-analysis, studies evaluating multiple treatment comparisons are modeled simultaneously, and estimation is informed by a combination of direct and indirect evidence. Network meta-analysis relies on an assumption of consistency, meaning that direct and indirect evidence should agree for each treatment comparison. Here we propose new local and global tests for inconsistency and demonstrate their application to three example networks. Because inconsistency is a property of a loop of treatments in the network meta-analysis, we locate the local test in a loop. We define a model with one inconsistency parameter that can be interpreted as loop inconsistency. The model builds on the existing ideas of node-splitting and side-splitting in network meta-analysis. To provide a global test for inconsistency, we extend the model across multiple independent loops with one degree of freedom per loop. We develop a new algorithm for identifying independent loops within a network meta-analysis. Our proposed models handle treatments symmetrically, locate inconsistency in loops rather than in nodes or treatment comparisons, and are invariant to choice of reference treatment, making the results less dependent on model parameterization. For testing global inconsistency in network meta-analysis, our global model uses fewer degrees of freedom than the existing design-by-treatment interaction approach and has the potential to increase power. To illustrate our methods, we fit the models to three network meta-analyses varying in size and complexity. Local and global tests for inconsistency are performed and we demonstrate that the global model is invariant to choice of independent loops.
在网状荟萃分析中，同时对多个治疗比较的研究进行建模，并通过直接和间接证据的综合来进行估计。网状荟萃分析依赖于一致性假设，即对于每个治疗比较，直接证据和间接证据应该一致。在这里，我们提出了新的一致性和非一致性的局部和全局检验，并将其应用于三个示例网络。由于不一致性是网状荟萃分析中治疗环路的特性，因此我们将局部检验定位在环路中。我们定义了一个具有一个不一致参数的模型，该参数可以解释为环路不一致性。该模型建立在网状荟萃分析中节点分裂和边分裂的现有思想基础上。为了提供不一致性的全局检验，我们通过一个自由度扩展了模型到多个独立的环路中。我们开发了一种新的算法来识别网状荟萃分析中的独立环路。我们提出的模型对称地处理治疗方法，将不一致性定位在环路中，而不是在节点或治疗比较中，并且对参考治疗的选择不变，从而减少了对模型参数化的依赖。对于网状荟萃分析中的全局不一致性检验，我们的全局模型比现有的基于治疗设计的交互作用方法使用的自由度更少，并且有可能提高功效。为了说明我们的方法，我们将模型拟合到三个大小和复杂性不同的网状荟萃分析中。进行了不一致性的局部和全局检验，并演示了全局模型对独立环路的选择是不变的。
