Department of Primary Education, School of Education, University of Ioannina, Ioannina, Greece.
Data Science and Advanced Analytics, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Res Synth Methods. 2023 May;14(3):382-395. doi: 10.1002/jrsm.1617. Epub 2023 Jan 4.
Network meta-analysis (NMA) is an established method for assessing the comparative efficacy and safety of competing interventions. It is often the case that we deal with interventions that consist of multiple, possibly interacting, components. Examples of interventions' components include characteristics of the intervention, mode (face-to-face, remotely etc.), location (hospital, home etc.), provider (physician, nurse etc.), time of communication (synchronous, asynchronous etc.) and other context related components. Networks of multicomponent interventions are typically sparse and classical NMA inference is not straightforward and prone to confounding. Ideally, we would like to disentangle the effect of each component to find out what works (or does not work). To this aim, we propose novel ways of visualizing the NMA results, describe their use, and illustrate their application in real-life examples. We developed an R package viscomp to produce all the suggested figures.
网络荟萃分析（NMA）是一种评估竞争干预措施相对疗效和安全性的成熟方法。我们经常需要处理由多个、可能相互作用的组件组成的干预措施。干预措施组件的示例包括干预措施的特征、方式（面对面、远程等）、地点（医院、家庭等）、提供者（医生、护士等）、沟通时间（同步、异步等）和其他与背景相关的组件。多组件干预措施的网络通常是稀疏的，经典的 NMA 推断并不直接，容易产生混杂。理想情况下，我们希望分解每个组件的效果，以找出哪些有效（或无效）。为此，我们提出了可视化 NMA 结果的新方法，描述了它们的用途，并在实际示例中说明了它们的应用。我们开发了一个 R 包 viscomp 来生成所有建议的图形。
