Suppr超能文献

系统评价和荟萃分析中使用多个数据源的实用指南(来自 MUDS 研究的实例)。

Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study).

机构信息

Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA.

出版信息

Res Synth Methods. 2018 Mar;9(1):2-12. doi: 10.1002/jrsm.1277. Epub 2017 Dec 15.

Abstract

Data for individual trials included in systematic reviews may be available in multiple sources. For example, a single trial might be reported in 2 journal articles and 3 conference abstracts. Because of differences across sources, source selection can influence the results of systematic reviews. We used our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study, and evidence from previous studies, to develop practical guidance for using multiple data sources in systematic reviews. We recommend the following: (1) Specify which sources you will use. Before beginning a systematic review, consider which sources are likely to contain the most useful data. Try to identify all relevant reports and to extract information from the most reliable sources. (2) Link individual trials with multiple sources. Write to authors to determine which sources are likely related to the same trials. Use a modified Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flowchart to document both the selection of trials and the selection of sources. (3) Follow a prespecified protocol for extracting trial characteristics from multiple sources. Identify differences among sources, and contact study authors to resolve differences if possible. (4) Prespecify outcomes and results to examine in the review and meta-analysis. In your protocol, describe how you will handle multiple outcomes within each domain of interest. Look for outcomes using all eligible sources. (5) Identify which data sources were included in the review. Consider whether the results might have been influenced by data sources used. (6) To reduce bias, and to reduce research waste, share the data used in your review.

摘要

纳入系统评价的单个试验数据可能来自多个来源。例如,单个试验可能在 2 篇期刊文章和 3 篇会议摘要中报告。由于来源之间存在差异,因此来源选择可能会影响系统评价的结果。我们在多数据源系统评价(MUDS)研究中积累了经验,并借鉴了之前的研究证据,为系统评价中使用多个数据源提供了实用指南。我们建议:(1)指定要使用的来源。在开始系统评价之前,请考虑哪些来源可能包含最有用的数据。尽量识别所有相关报告,并从最可靠的来源中提取信息。(2)将单个试验与多个来源联系起来。给作者写信,以确定哪些来源可能与同一试验有关。使用修改后的系统评价和荟萃分析的首选报告项目(PRISMA)流程图来记录试验和来源的选择。(3)按照从多个来源提取试验特征的既定方案进行操作。识别来源之间的差异,并在可能的情况下与研究作者联系以解决差异。(4)预先指定要在综述和荟萃分析中检查的结局和结果。在方案中,描述您将如何处理每个感兴趣领域内的多个结局。使用所有合格的来源寻找结局。(5)确定纳入综述的数据源。考虑使用的数据源是否可能影响结果。(6)为了减少偏倚和研究浪费,请共享您在综述中使用的数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fa95/5888128/f1670d5dbfa8/JRSM-9-2-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验