Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a more restrictive form of additivity than the other which we term an anchored and unanchored model, respectively. We show that an anchored model can provide a poor fit to the data if it is misspecified. Second, given that Bayesian models are often preferred by practitioners, we develop two novel unanchored Bayesian CNMA models presented under the unified notation. An extensive simulation study examining bias, coverage probabilities, and treatment rankings confirms the favorable performance of the novel models. This is the first simulation study to compare the statistical properties of CNMA models in the literature. Finally, the use of our novel models is demonstrated on a real dataset, and the results of CNMA models on the dataset are compared.

中文翻译：

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用于组件网络元分析的贝叶斯非锚定加性模型

组件网络荟萃分析 (CNMA) 模型是标准网络荟萃分析 (NMA) 模型的扩展，它解释了网络中多组件处理的使用。本文对 CNMA 的几个统计方面做出了创新贡献。首先，通过引入一个统一的符号，我们确定当前可用的方法在它们假设可加性的方式上有所不同，这是迄今为止在文献中被忽视的一个重要区别。特别是，一个模型使用比另一个模型更严格的可加性形式，我们分别将其称为锚定模型和非锚定模型。我们表明，如果指定错误，锚定模型可能无法很好地拟合数据。其次，鉴于贝叶斯模型通常受到从业者的青睐，我们开发了两种新颖的未锚定贝叶斯 CNMA 模型，以统一符号表示。一项检查偏差、覆盖概率和治疗排名的广泛模拟研究证实了新模型的良好性能。这是第一个比较文献中CNMA模型统计特性的模拟研究。最后，在真实数据集上演示了我们新模型的使用，并比较了 CNMA 模型在数据集上的结果。