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Research techniques made simple: workflow for searching databases to reduce evidence selection bias in systematic reviews

机译:研究技术变得简单:用于搜索数据库以减少系统评价中证据选择偏见的工作流程

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摘要

Clinical trials and basic science studies without statistically significant results are less likely to be published than studies with statistically significant results. Systematic reviews and meta-analyses that omit unpublished data are at high risk of distorted conclusions. Here, we describe methods to search beyond bibliographical databases to reduce evidence selection bias in systematic reviews. Unpublished studies may be identified by searching conference proceedings. Moreover, clinical trial registries—databases of planned and ongoing trials—and regulatory agency websites such as the European Medicine Agency (EMA) and the United States Food and Drug Administration (FDA) may provide summaries of efficacy and safety data. Primary and secondary outcomes are prespecified in trial registries, thus allowing the assessment of outcome reporting bias by comparison with the trial report. The sources of trial data and documents are still evolving, with ongoing initiatives promoting broader access to clinical study reports and individual patient data. There is currently no established methodology to ensure that the multiple sources of information are incorporated. Nonetheless, systematic reviews must adapt to these improvements and cover the new sources in their search strategies.
机译:没有统计学显着性结果的临床试验和基础科学研究比具有统计学显着性结果的研究更不可能发表。忽略未发表数据的系统评价和荟萃分析极有可能导致结论失真。在这里,我们描述了在书目数据库之外进行搜索以减少系统评价中证据选择偏见的方法。未发表的研究可以通过搜索会议记录来确定。此外,临床试验注册管理机构(计划中的和正在进行的试验的数据库)以及监管机构的网站(例如欧洲医学局(EMA)和美国食品药品管理局(FDA))可以提供功效和安全性数据的摘要。在试验注册中心中预先指定了主要和次要结果,因此可以通过与试验报告进行比较来评估结果报告的偏倚。试验数据和文件的来源仍在不断发展,并且正在进行的举措促进了对临床研究报告和患者个人数据的更广泛访问。当前,尚无确定的方法来确保纳入多种信息来源。尽管如此,系统的评论必须适应这些改进,并在其搜索策略中覆盖新的资源。

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