在网络元分析中对治疗方法的排名:纳入最少重要的差异
Tristan Curteis1,2, Augustine Wigle3, Christopher J Michaels4
1Costello Medical, 50-60 Station Road, Cambridge, CB1 2JH, UK. tristan.curteis@costellomedical.com.
BMC medical research methodology
|March 11, 2025
概括
网络元分析 (NMA) 排名指标现在可以结合最小重要的差异 (MID) 以获得更具临床相关性的结果. 本研究介绍了MID调整的指标和贝叶斯软件实现,以改善治疗比较.
科学领域:
- 生物统计学 生物统计学
- 卫生经济学 卫生经济学
- 临床流行病学临床流行病学
背景情况:
- 标准网络元分析 (NMA) 排名指标,如Sucra,往往不考虑治疗效应的程度.
- 这种监督可能会导致排名不反映患者和临床医生的临床意义上的差异.
- 最少重要的差异 (MID) 代表结果中最小的有意义的变化,但它们在NMA排名中的整合尚未得到充分探索.
研究的目的:
- 开发和介绍新的NMA排名指标,这些指标包括最小重要的差异 (MID).
- 在考虑MID时提供处理排名关系的方法.
- 为这些调整的排名指标提供贝叶斯软件实现.
主要方法:
- 定义的MID调整版本的常见NMA排名指标,包括中位数等级,最好的概率和SUCRAs.
- 讨论了处理排名中关系的方法,证明中点方法保留了平均SU CRA属性.
- 开发了一个贝叶斯软件包 (mid.nma.rank) 来实现这些MID调整的指标.
主要成果:
- 介绍了MID调整的排名指标的两个现实应用,使用已公布的NMA数据用于糖尿病和帕金森病治疗.
- 通过从现有文献中选择相关的MID来说明MID调整的指标的解释.
- 证明了在临床环境中提出的方法的实际实用性.
结论:
- 忽视NMA中的MID可以导致临床上无关紧要的治疗排名.
- 拟议的MID调整的贝叶斯排名指标提供了更为临床导向的方法.
- 中点绑定处理方法确保了可比性,R包 mid.nma.rank促进了易于应用.
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