Chu Haitao, Lin Lifeng, Wang Zheng, Wang Zilin, Chen Yong, Cappelleri Joseph C
Statistical Research and Data Science Center, Pfizer Inc., New York, New York, USA.
Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455 USA.
Wiley Interdiscip Rev Comput Stat. 2024 Jan-Feb;16(1). doi: 10.1002/wics.1639. Epub 2023 Nov 21.
Network meta-analysis (NMA) is a statistical procedure to simultaneously compare multiple interventions. Despite the added complexity of performing an NMA compared with the traditional pairwise meta-analysis, under proper assumptions the NMA can lead to more efficient estimates on the comparisons of interventions by combining and contrasting the direct and indirect evidence into a form of evidence that can be used to underpin treatment guidelines. Two broad classes of NMA methods are commonly used in practice: the contrast-based (CB-NMA) and the arm-based (AB-NMA) models. While CB-NMA only focuses on the relative effects by assuming fixed intercepts, the AB-NMA offers greater flexibility on the estimands, including both the absolute and relative effects by assuming random intercepts. A major criticism of the AB-NMA, on which we aim to elaborate in this paper, is that it does not retain randomization within trials, which may introduce bias in the estimated relative effects in some scenarios. This criticism was drawn under the implicit assumption that a given relative effect is transportable, in which case the data generating mechanism favors the inference based on CB-NMA, which models the relative effect. In this article, we aim to review, summarize, and elaborate on the underlying assumptions, similarities and differences, and also the advantages and disadvantages, between CB-NMA and AB-NMA methods. As indirect treatment comparison is susceptible to risk of bias no matter which approach is taken, it is important to consider both approaches in practice as complementary sensitivity analyses and to provide the totality of evidence from the data.
网络荟萃分析（NMA）是一种同时比较多种干预措施的统计方法。尽管与传统的成对荟萃分析相比，进行NMA的复杂性增加，但在适当的假设下，NMA可以通过将直接证据和间接证据结合并对比为一种可用于支持治疗指南的证据形式，从而在干预措施比较方面得出更有效的估计。在实践中通常使用两大类NMA方法：基于对比的（CB-NMA）和基于臂的（AB-NMA）模型。虽然CB-NMA通过假设固定截距仅关注相对效应，但AB-NMA在估计量方面提供了更大的灵活性，通过假设随机截距包括绝对效应和相对效应。我们旨在在本文中详细阐述的对AB-NMA的一个主要批评是，它没有保留试验中的随机化，这在某些情况下可能会在估计的相对效应中引入偏差。这种批评是在给定相对效应是可转移的隐含假设下得出的，在这种情况下，数据生成机制有利于基于对相对效应进行建模的CB-NMA的推断。在本文中，我们旨在回顾、总结和详细阐述CB-NMA和AB-NMA方法之间的潜在假设、异同以及优缺点。由于无论采用哪种方法，间接治疗比较都容易受到偏差风险的影响，因此在实践中将这两种方法作为互补的敏感性分析加以考虑并提供数据的全部证据非常重要。
