Component network meta‐analysis compared to a matching method in a disconnected network: A case study

观察研究 匹配(统计) 荟萃分析 倾向得分匹配 统计 计算机科学 网络分析 组分(热力学) 数学 计量经济学 医学 物理 量子力学 热力学 内科学
作者
Gerta Rücker,Susanne Schmitz,Guido Schwarzer
出处
期刊:Biometrical Journal [Wiley]
卷期号:63 (2): 447-461 被引量:29
标识
DOI:10.1002/bimj.201900339
摘要

Network meta-analysis is a method to combine evidence from randomized controlled trials (RCTs) that compare a number of different interventions for a given clinical condition. Usually, this requires a connected network. A possible approach to link a disconnected network is to add evidence from nonrandomized comparisons, using propensity score or matching-adjusted indirect comparisons methods. However, nonrandomized comparisons may be associated with an unclear risk of bias. Schmitz et al. used single-arm observational studies for bridging the gap between two disconnected networks of treatments for multiple myeloma. We present a reanalysis of these data using component network meta-analysis (CNMA) models entirely based on RCTs, utilizing the fact that many of the treatments consisted of common treatment components occurring in both networks. We discuss forward and backward strategies for selecting appropriate CNMA models and compare the results to those obtained by Schmitz et al. using their matching method. CNMA models provided a good fit to the data and led to treatment rankings that were similar, though not fully equal to that obtained by Schmitz et al. We conclude that researchers encountering a disconnected network with treatments in different subnets having common components should consider a CNMA model. Such models, exclusively based on evidence from RCTs, are a promising alternative to matching approaches that require additional evidence from observational studies. CNMA models are implemented in the R package netmeta.
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