GRADE在网状Meta分析中应用的基本原理和方法介绍

被引：15

作者：

杨楠 ^{[1
]}

肖淑君 ^{[2
]}

周奇 ^{[2
]}

俞阳 ^{[2
]}

姚亮 ^{[3
,4
,5
]}

陈耀龙 ^{[3
,4
,5
]}

田金徽 ^{[3
,4
,5
]}

李伦 ^{[1
,3
,4
,5
]}

杨克虎 ^{[3
,4
,5
]}

机构：

[1] 不详

[2] 兰州大学第一临床医学院

[3] 不详

[4] 兰州大学基础医学院

[5] 甘肃省循证医学与临床转化重点实验室

[6] 兰州大学循证医学中心,兰州大学基础医学院

[7] GRADE中国中心,兰州大学

[8] 不详

来源：

中国循证医学杂志 | 2016年 / 05期

关键词：

GRADE; 网状Meta分析; 证据质量;

D O I：

暂无

中图分类号：

R-03 [];

学科分类号：

摘要：

网状Meta分析的最大优势在于可以量化比较同类疾病的不同干预措施,合并直接比较和间接比较证据,并按照某一结果指标的优劣排序,从而优选最佳的方案。本文结合GRADE工作组最新发表的文章以及其他相关研究,介绍GRADE在网状Meta分析中应用的原理和方法。GRADE工作组对网状Meta分析证据分级主要分为四个步骤:首先呈现两个干预措施之间直接和间接比较的效应量和可信区间,其次分别对其进行证据质量评估,再次呈现网状Meta分析的结果,最后评估网状Meta分析结果的证据质量。直接证据的评估参考GRADE在传统Meta分析中的应用方法。间接证据的评估依据产生间接结果的直接比较中证据质量低的组别。基于直接比较和间接比较网状Meta分析结果的证据质量取二者证据质量高的组别作为网状Meta分析的证据级别。GRADE工作组提出了对网状Meta分析进行证据质量分级的四步法,进一步完善了GRADE在网状Meta分析中运用的理论基础。但具体分级的过程中,分级人员需要熟悉GRADE的基本理论,并进行预试验,以保证对分级标准理解的一致性。此外还需要考虑间接比较中不同组别间的不可传递性以及直接比较和间接比较结果的不一致性。

引用

页码：598 / 603

页数：6

共 27 条

[11] Unsolved issues of mixed treatment comparison meta‐analysis: network size and inconsistency[J] . Sibylle Sturtz,Ralf Bender.Res. Syn. Meth. . 2012 (4)
[12] Consistency and inconsistency in network meta‐analysis: concepts and models for multi‐arm studies[J] . J. P. T. Higgins,D Jackson,J. K. Barrett,G Lu,A. E. Ades,I. R. White.Res. Syn. Meth. . 2012 (2)
[13] Comparative Effectiveness of Drug Treatments to Prevent Fragility Fractures: A Systematic Review and Network Meta-Analysis[J] . Mohammad Hassan Murad,Matthew T. Drake,Rebecca J. Mullan,Karen F. Mauck,Louise M. Stuart,Melanie A. Lane,Nisrin O. Abu Elnour,Patricia J. Erwin,Ahmad Hazem,Milo A. Puhan,Tianjing Li,Victor M. Montori.The Journal of Clinical Endocrinology ＆ Metabolism . 2012 (6)
[14] Consistency between Direct and Indirect Trial Evidence: Is Direct Evidence Always More Reliable?[J] . Jason Madan,Matt D. Stevenson,Katy L. Cooper,A.E. Ades,Sophie Whyte,Ron Akehurst.Value in Health . 2011 (6)
[15] Conducting Indirect-Treatment-Comparison and Network-Meta-Analysis Studies: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices—Part 2[J] . David C. Hoaglin,Neil Hawkins,Jeroen P. Jansen,David A. Scott,Robbin Itzler,Joseph C. Cappelleri,Cornelis Boersma,David Thompson,Kay M. Larholt,Mireya Diaz,Annabel Barrett.Value in Health . 2011 (4)
[16] Interpreting Indirect Treatment Comparisons and Network Meta-Analysis for Health-Care Decision Making: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 1[J] . Jeroen P. Jansen,Rachael Fleurence,Beth Devine,Robbin Itzler,Annabel Barrett,Neil Hawkins,Karen Lee,Cornelis Boersma,Lieven Annemans,Joseph C. Cappelleri.Value in Health . 2011 (4)
[17] GRADE guidelines: 8. Rating the quality of evidence—indirectness[J] . Gordon H. Guyatt,Andrew D. Oxman,Regina Kunz,James Woodcock,Jan Brozek,Mark Helfand,Pablo Alonso-Coello,Yngve Falck-Ytter,Roman Jaeschke,Gunn Vist,Elie A. Akl,Piet N. Post,Susan Norris,Joerg Meerpohl,Vijay K. Shukla,Mona Nasser,Holger J. Schünemann.Journal of Clinical Epidemiology . 2011 (12)
[18] GRADE guidelines: 7. Rating the quality of evidence—inconsistency[J] . Gordon H. Guyatt,Andrew D. Oxman,Regina Kunz,James Woodcock,Jan Brozek,Mark Helfand,Pablo Alonso-Coello,Paul Glasziou,Roman Jaeschke,Elie A. Akl,Susan Norris,Gunn Vist,Philipp Dahm,Vijay K. Shukla,Julian Higgins,Yngve Falck-Ytter,Holger J. Schünemann.Journal of Clinical Epidemiology . 2011 (12)
[19] GRADE guidelines: 4. Rating the quality of evidence—study limitations （risk of bias）[J] . Gordon H. Guyatt,Andrew D. Oxman,Gunn Vist,Regina Kunz,Jan Brozek,Pablo Alonso-Coello,Victor Montori,Elie A. Akl,Ben Djulbegovic,Yngve Falck-Ytter,Susan L. Norris,John W. Williams,David Atkins,Joerg Meerpohl,Holger J. Schünemann.Journal of Clinical Epidemiology . 2011 (4)
[20] GRADE guidelines: 3. Rating the quality of evidence[J] . Howard Balshem,Mark Helfand,Holger J. Schünemann,Andrew D. Oxman,Regina Kunz,Jan Brozek,Gunn E. Vist,Yngve Falck-Ytter,Joerg Meerpohl,Susan Norris,Gordon H. Guyatt.Journal of Clinical Epidemiology . 2011 (4)

← 1 2 3 →