Abstract In network meta‐analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta‐analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components.

中文翻译：

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多因素干预的网络荟萃分析

摘要 在网络荟萃分析 (NMA) 中，治疗可能是复杂的干预措施，例如，某些治疗可能是其他治疗或共同成分的组合。在标准 NMA 中，所有现有（单一或组合）处理都是网络中的不同节点。然而，有时另一种模型是有趣的，它利用一些治疗是常见成分的组合的信息，称为成分网络元分析 (CNMA) 模型。加性 CNMA 模型假设组合 A 和 B 两种成分的处理效果是 A 和 B 效果的总和，这很容易扩展到由两种以上成分组成的处理。这意味着在比较中相等的分量相互抵消。交互 CNMA 模型还允许组件之间的交互。已经提出了贝叶斯分析。我们在常客 R 包 netmeta 中报告了 CNMA 模型的实现。使用加权最小二乘回归估计所有参数。我们使用 NMA 治疗初级保健中的抑郁症来说明 CNMA 模型的应用。此外，我们表明，如果子网络中的复合处理包含公共组件，这些模型甚至可以应用于断开连接的网络。