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疼痛、麻醉和围手术期医学中随机对照试验的网络荟萃分析方法:叙述性综述

Methodologies for network meta-analysis of randomised controlled trials in pain, anaesthesia, and perioperative medicine: a narrative review.

作者信息

Doleman Brett, Jakobsen Janus Christian, Mathiesen Ole, Cooper Nicola, Sutton Alex, Hardman Jonathan

机构信息

University of Nottingham, Nottingham, UK.

Copenhagen Trial Unit, Centre for Clinical Intervention Research, Capital Region of Denmark & Department of Regional Health Research, The Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.

出版信息

Br J Anaesth. 2025 Apr;134(4):1029-1040. doi: 10.1016/j.bja.2024.12.039. Epub 2025 Feb 19.

Abstract

Network meta-analysis has emerged as a method for analysing clinical trials, with a large increase in the number of publications over the past decade. Network meta-analysis offers advantages over traditional pairwise meta-analysis, including increased power, the ability to compare treatments not compared in the original trials, and the ability to rank treatments. However, network meta-analyses are inherently more complex than pairwise meta-analyses, requiring additional statistical expertise and assumptions. Many factors can affect the certainty of evidence from pairwise meta-analysis and can often lead to unreliable results. Network meta-analysis is prone to all these issues, although it has the additional assumption of transitivity. Here we review network meta-analyses, problems with their conduct and reporting, and methodological strategies that can be used by those conducting reviews to help improve the reliability of their findings. We provide evidence that violation of the assumption of transitivity is relatively common and inadequately considered in published network meta-analyses. We explain key concepts with clinically relevant examples for those unfamiliar with network meta-analysis to facilitate their appraisal and application of their results to clinical practice.

摘要

网络荟萃分析已成为一种分析临床试验的方法,在过去十年中,相关出版物的数量大幅增加。与传统的成对荟萃分析相比,网络荟萃分析具有诸多优势,包括检验效能提高、能够比较原始试验中未作比较的治疗方法以及能够对治疗方法进行排序。然而,网络荟萃分析本质上比成对荟萃分析更为复杂,需要额外的统计专业知识和假设。许多因素会影响成对荟萃分析证据的确定性,并且常常会导致不可靠的结果。网络荟萃分析也容易出现所有这些问题,尽管它还有传递性这一额外假设。在此,我们对网络荟萃分析、其实施和报告中存在的问题,以及进行综述的人员可用于帮助提高研究结果可靠性的方法策略进行综述。我们提供的证据表明,在已发表的网络荟萃分析中,违背传递性假设的情况相对常见且未得到充分考虑。我们用临床相关实例解释关键概念,以便不熟悉网络荟萃分析的人员能够评估其结果并将其应用于临床实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a095/11947594/2ba7c23f54d3/gr1.jpg

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