IF 3.9 Q1 SPORT SCIENCES BMJ Open Sport & Exercise Medicine Pub Date : 2024-12-22 eCollection Date: 2024-01-01 DOI:10.1136/bmjsem-2024-002262
Daniel L Belavý, Svenja Kaczorowski, Tobias Saueressig, Patrick J Owen, Adriani Nikolakopoulou
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引用次数: 0

摘要

网络荟萃分析(NMA)在运动与锻炼医学(SEM)研究中的应用不断增加,因为它可以对可能未在单一随机对照试验中进行评估的多种干预措施进行比较。然后,NMA 可以为临床医生提供可能更好的干预措施。尽管 NMA 的使用越来越多,但我们注意到,在 SEM 领域,作者小组面临的一个主要挑战可能是关键假设的评估和报告,尤其是过渡性和一致性。本文为 SEM 研究人员提供了一份实用指南,指导他们如何处理 NMA 的传递性和一致性假设。我们利用之前发表的 SEM 领域的 NMA,提供了统计代码、源数据和工作示例,以促进对特定领域 NMA 的理解和最佳实践。我们希望这些资源能够改进 NMA 的实施和报告,最终推动 SEM 领域的进步。
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How to conduct and report checking transitivity and inconsistency in network-meta-analysis: a narrative review including practical worked examples, code and source data for sports and exercise medicine researchers.

The use of network meta-analysis (NMA) in sport and exercise medicine (SEM) research continues to rise as it enables the comparison of multiple interventions that may not have been assessed in a single randomised controlled trial. NMA can then inform clinicians on potentially better interventions. Despite the increased use of NMA, we have observed that in the SEM field, a key challenge for author groups can be the assessment and reporting of key assumptions, in particular transitivity and consistency. This paper provides SEM researchers with a practical guide on how to approach the transitivity and consistency assumptions of NMA. Using a previously published NMA in the SEM field, we provide the statistical code, source data and worked examples to facilitate understanding and best practice of NMA in the particular field. We hope these resources result in improved conduct and reporting of NMA that ultimately leads to advances in the SEM field.

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来源期刊
CiteScore
7.10
自引率
4.20%
发文量
106
审稿时长
20 weeks
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