Belavý Daniel L, Kaczorowski Svenja, Saueressig Tobias, Owen Patrick J, Nikolakopoulou Adriani
Department für Pflege-, Hebammen- und Therapiewissenschaften, Hochschule für Gesundheit, Bochum, Germany.
Physio Meets Science GmbH, Heidelberg, Germany.
BMJ Open Sport Exerc Med. 2024 Dec 22;10(4):e002262. doi: 10.1136/bmjsem-2024-002262. eCollection 2024.
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.
网络荟萃分析（NMA）在运动与运动医学（SEM）研究中的应用持续增加，因为它能够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有关潜在更好干预措施的信息。尽管NMA的使用有所增加，但我们观察到，在SEM领域，作者团队面临的一个关键挑战可能是对关键假设的评估和报告，特别是传递性和一致性。本文为SEM研究人员提供了一份实用指南，介绍如何处理NMA的传递性和一致性假设。利用SEM领域先前发表的一项NMA，我们提供了统计代码、源数据和实例，以促进对该特定领域NMA的理解和最佳实践。我们希望这些资源能改进NMA的实施和报告，最终推动SEM领域的发展。
