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

机译:在系统评价和荟萃分析中使用多个数据源的实用指南(以MUDS研究为例)

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摘要

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.
机译:系统评价中包含的个别试验数据可能有多种来源。例如,可能在2篇期刊文章和3篇会议摘要中报告一次试验。由于来源之间的差异,来源选择可能会影响系统评价的结果。我们利用我们在系统评价中的多个数据源(MUDS)研究中的经验以及先前研究的证据,来开发在系统评价中使用多个数据源的实用指南。我们建议以下内容:(1)指定您将使用哪些来源。在开始系统审查之前,请考虑哪些来源可能包含最有用的数据。尝试识别所有相关报告,并从最可靠的来源中提取信息。 (2)将个别试验与多种来源联系起来。写信给作者,以确定哪些来源可能与同一试验有关。使用经过修改的“系统评价和荟萃分析首选报告项目”(PRISMA)流程图来记录试验选择和来源选择。 (3)遵循预先规定的协议从多个来源提取试验特征。确定出处之间的差异,并在可能的情况下联系研究作者以解决差异。 (4)预先指定结果和结果,以便在审查和荟萃分析中进行检查。在您的方案中,描述您将如何处理每个感兴趣领域内的多种结果。使用所有合格来源寻找结果。 (5)确定在审查中包括哪些数据源。考虑结果是否可能受到所用数据源的影响。 (6)为减少偏见并减少研究浪费,请共享您的审阅中使用的数据。

著录项

  • 来源
    《Research Synthesis Methods》 |2018年第1期|2-12|共11页
  • 作者单位

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

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

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

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

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    meta-analysis; multiple data sources; reporting bias; risk of bias assessment; selective outcome reporting;

    机译:荟萃分析多个数据源;报告偏见;偏见评估的风险;选择性结果报告;
  • 入库时间 2022-08-17 23:54:47

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