标题： 贝叶斯非锚定加法模型用于成分网络荟萃分析 显示英文标题

标题： Bayesian unanchored additive models for component network meta-analysis

摘要： 组件网络荟萃分析（CNMA）模型是对标准网络荟萃分析（NMA）模型的扩展，它考虑了网络中多组分治疗的使用。 本文在CNMA的几个统计方面做出了创新贡献。 首先，通过引入统一的符号表示，我们确立了目前可用的方法在假设可加性的方式上存在差异，这是一个在文献中至今被忽视的重要区别。 特别是，一个模型使用的可加性形式比另一个模型更为严格，我们分别称其为锚定模型和非锚定模型。 我们表明，如果锚定模型被错误指定，它可能无法很好地拟合数据。 其次，鉴于贝叶斯模型通常受到实践者的青睐，我们开发了两种新颖的非锚定贝叶斯CNMA模型，并在统一符号表示下进行介绍。 一项广泛的模拟研究检查了偏差、覆盖概率和治疗排名，证实了新模型的优越性能。 这是第一项在文献中比较CNMA模型统计特性的模拟研究。 最后，我们在真实数据集上展示了我们新模型的应用，并比较了该数据集上CNMA模型的结果。 显示英文摘要

摘要： 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.