Papakonstantinou Theodoros, Nikolakopoulou Adriani, Higgins Julian P T, Egger Matthias, Salanti Georgia
Institute of Social and Preventive Medicine University of Bern Bern Switzerland.
Population Health Sciences, Bristol Medical School University of Bristol Bristol UK.
Campbell Syst Rev. 2020 Mar 11;16(1):e1080. doi: 10.1002/cl2.1080. eCollection 2020 Mar.
Network meta-analysis (NMA) compares several interventions that are linked in a network of comparative studies and estimates the relative treatment effects between all treatments, using both direct and indirect evidence. NMA is increasingly used for decision making in health care, however, a user-friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking. This paper is a tutorial describing the Confidence In Network Meta-Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, , 9, e99682) and refined by Nikolakopoulou et al (2019, ). Six domains that affect the level of confidence in the NMA results are considered: (a) within-study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence. CINeMA is freely available and open-source and no login is required. In the configuration step users upload their data, produce network plots and define the analysis and effect measure. The dataset should include assessments of study-level risk of bias and judgments on indirectness. CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity. Users are then guided through a systematic evaluation of the six domains. In this way reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to "no concerns," "some concerns," or "major concerns" in each of the six domains, which are graphically summarized on the report page for all effect estimates. Finally, judgments across the domains are summarized into a single confidence rating ("high," "moderate," "low," or "very low"). In conclusion, the user-friendly web-based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.
网络荟萃分析（NMA）比较了在比较研究网络中相互关联的几种干预措施，并利用直接和间接证据估计所有治疗方法之间的相对治疗效果。NMA在医疗保健决策中的应用越来越广泛，然而，目前缺乏一个用户友好的系统来评估对NMA结果的可信度。本文是一篇教程，介绍了网络荟萃分析置信度（CINeMA）网络应用程序，该程序基于Salanti等人（2014年，9卷，e99682）开发并经Nikolakopoulou等人（2019年）完善的框架。考虑了影响NMA结果置信度水平的六个领域：（a）研究内偏倚，（b）报告偏倚，（c）间接性，（d）不精确性，（e）异质性，以及（f）不一致性。CINeMA免费提供且开源，无需登录。在配置步骤中，用户上传数据、生成网络图并定义分析和效应测量方法。数据集应包括对研究水平偏倚风险的评估以及对间接性的判断。CINeMA调用R中的netmeta程序来估计相对效应和异质性。然后，引导用户对这六个领域进行系统评估。通过这种方式，评审人员评估NMA中每个相对治疗效果在六个领域中引起“无担忧”、“有些担忧”或“主要担忧”的担忧程度，并在报告页面上以图形方式汇总所有效应估计值。最后，将各领域的判断汇总为单一的置信度评级（“高”、“中”、“低”或“极低”）。总之，基于网络的用户友好型CINeMA平台提供了一个透明的框架，用于评估来自多种干预措施的系统评价证据。
