网络荟萃分析二十年:持续争议与最新发展。

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Research Synthesis Methods Pub Date : 2024-01-18 DOI:10.1002/jrsm.1700
A. E. Ades, Nicky J. Welton, Sofia Dias, David M. Phillippo, Deborah M. Caldwell
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引用次数: 0

摘要

网络荟萃分析(NMA)是成对荟萃分析(PMA)的延伸,它将多种治疗方法的试验证据整合到连接的网络中。NMA 提供理性决策所需的内部一致的相对疗效估计值。在最初的 20 年中,NMA 的应用呈指数级增长,主要应用于健康技术评估 (HTA)(主要是报销决策和临床指南制定)和临床研究出版物。这一时期是荟萃分析的转型期,先是起源于教育学和社会心理学,通过探索大型异质数据集来寻找效应修饰因子,然后是临床医学中平均少于六项研究的小型成对荟萃分析。随后,在缺乏直接比较或只有一两项研究提供信息的稀疏网络中,对特定人群特定剂量的特定治疗效果进行了狭义的估计。NMA 是一种强大而成熟的技术,但尽管其应用呈指数级增长,人们对 NMA 的可靠性和有效性的怀疑依然存在。在此,我们概述了持续存在的争议,并回顾了一些最新进展。我们建议应尽量减少异质性,因为它对 NMA 的可靠性构成了威胁,而这种威胁尚未得到充分认识,这可能是因为在 PMA 中异质性未被视为一个问题。需要对决策所用数据集的异质性和不一致性程度、基于 NMA 提出建议的正式方法以及多层次网络元回归的进一步发展开展更多研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Twenty years of network meta-analysis: Continuing controversies and recent developments

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
期刊最新文献
Issue Information A tutorial on aggregating evidence from conceptual replication studies using the product Bayes factor Evolving use of the Cochrane Risk of Bias 2 tool in biomedical systematic reviews Exploring methodological approaches used in network meta-analysis of psychological interventions: A scoping review An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research
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