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肝脏病学领域系统评价和Meta分析的偏倚风险评估

Assessment for Risk of Bias in Systematic Reviews and Meta-Analyses in the Field of Hepatology.

作者信息

Kim Gaeun, Cho Youn Zoo, Baik Soon Koo

机构信息

Department of Nursing, Keimyung University College of Nursing, Daegu, Korea.

Department of Internal Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea.

出版信息

Gut Liver. 2015 Nov 23;9(6):701-6. doi: 10.5009/gnl14451.

DOI:10.5009/gnl14451
PMID:26503570
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4625697/
Abstract

A systematic review (SR) provides the best and most objective analysis of the existing evidence in a particular field. SRs and derived conclusions are essential for evidence-based strategies in medicine and evidence-based guidelines in clinical practice. The popularity of SRs has also increased markedly in the field of hepatology. However, although SRs are considered to provide a higher level of evidence with greater confidence than original articles, there have been no reports on the quality of SRs and meta-analyses (MAs) in the field of hepatology. Therefore, we performed a quality assessment of 225 SRs and MAs that were recently published in the field of hepatology (January 2011 to September 2014) using A MeaSurement Tool to Assess systematic Reviews (AMSTAR). Using AMSTAR, we revealed both a shortage of assessments of the scientific quality of individual studies and a publication bias in many SRs and MAs. This review addresses the concern that SRs and MAs need to be conducted in a stricter and more objective manner to minimize bias and random errors. Thus, SRs and MAs should be supported by a multidisciplinary approach that includes clinical experts, methodologists, and statisticians.

摘要

系统评价(SR)能对特定领域的现有证据进行最佳且最客观的分析。系统评价及其得出的结论对于医学领域基于证据的策略以及临床实践中的循证指南至关重要。系统评价在肝病学领域的受欢迎程度也显著提高。然而,尽管系统评价被认为比原始文章能提供更高水平的证据且可信度更高,但在肝病学领域尚无关于系统评价和荟萃分析(MA)质量的报告。因此,我们使用评估系统评价的测量工具(AMSTAR)对最近在肝病学领域发表的225篇系统评价和荟萃分析(2011年1月至2014年9月)进行了质量评估。通过AMSTAR,我们发现许多系统评价和荟萃分析既缺乏对个体研究科学质量的评估,又存在发表偏倚。本综述关注的是,系统评价和荟萃分析需要以更严格、更客观的方式进行,以尽量减少偏倚和随机误差。因此,系统评价和荟萃分析应得到包括临床专家、方法学家和统计学家在内的多学科方法的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5b6/4625697/e1ab4621df01/gnl-09-701f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5b6/4625697/e1ab4621df01/gnl-09-701f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5b6/4625697/e1ab4621df01/gnl-09-701f1.jpg

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