Network meta-analysis (NMA) is widely used in evidence synthesis to estimate the effects of several competing interventions for a given clinical condition. One of the challenges is that it is not possible in disconnected networks. Component network meta-analysis (CNMA) allows technically 'reconnecting' a disconnected network with multicomponent interventions. The additive CNMA model assumes that the effect of any multicomponent intervention is the additive sum of its components. This assumption can be relaxed by adding interaction component terms, which improves the goodness of fit but decreases the network connectivity. Model selection aims at finding the model with a reasonable balance between the goodness of fit and connectivity (selected CNMA model). We aim to introduce a forward model selection strategy for CNMA models and to investigate the performance of CNMA models for connected and disconnected networks. We applied the methods to a real Cochrane review dataset and simulated data with additive, mildly, or strongly violated intervention effects. We started with connected networks, and we artificially constructed disconnected networks. We compared the results of the additive and the selected CNMAs from each connected and disconnected network with the NMA using the mean squared error and coverage probability. CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. On the contrary, model selection does not perform well for disconnected networks, and we recommend conducting separate analyses of subnetworks.

翻译：在证据综合中,广泛使用网络元分析(NMA)来估计对特定临床条件的若干相互竞争的干预措施的效果。挑战之一是,在不连接的网络中不可能出现这种干预。元网络元分析(CNMA)允许在技术上“重新连接”一个与多个组成部分的干预互不连接的网络。添加的CNMA模型假定,任何多个组成部分的干预的效果都是其组成部分的添加总和。这一假设可以通过增加互动部分条款来放松,因为增加互动部分条款可以提高适合性,但降低了网络连接性。模型选择的目的是找到在适合性和连接性之间保持合理平衡的模型(选定的CNMA模型)。我们的目标是为CNMA模型模型引入前期模式选择战略,并调查CNMA模型与互连互连和互连的网络的性。我们应用了这种方法来实际的Cochrane审查数据集和模拟数据及其组成部分的添加、温和或严重违反干预效应。我们从连接的网络开始,人为地构建了互连通网络。我们比较了添加的添加和从每个与NMA系统连接和互连通的网络(选定的CNMA模型模型模型模型模型模型和覆盖范围)的模型可以提供良好的网络连接性分析。