American Institutes for Research, 1000 Thomas Jefferson St. NW, Washington, DC, 20007, USA.
University of North Carolina, Chapel Hill, USA.
Syst Rev. 2020 May 26;9(1):116. doi: 10.1186/s13643-020-01376-9.
Meta-analysts rely on the availability of data from previously conducted studies. That is, they rely on primary study authors to register their outcome data, either in a study's text or on publicly available websites, and report the results of their work, either again in a study's text or on publicly accessible data repositories. If a primary study author does not register data collection and similarly does not report the data collection results, the meta-analyst is at risk of failing to include the collected data. The purpose of this study is to attempt to locate one type of meta-analytic data: findings from studies that neither registered nor reported the collected outcome data. To do so, we conducted a large-scale search for potential studies and emailed an author query request to more than 600 primary study authors to ask if they had collected eligible outcome data. We received responses from 75 authors (12.3%), three of whom sent eligible findings. The results of our search confirmed our proof of concept (i.e., that authors collect data but fail to register or report it publicly), and the meta-analytic results indicated that excluding the identified studies would change some of our substantive conclusions. Cost analyses indicated, however, a high price to finding the missing studies. We end by reaffirming our calls for greater adoption of primary study pre-registration as well as data archiving in publicly available repositories.
元分析人员依赖于先前进行的研究的数据可用性。也就是说，他们依赖于主要研究作者在研究文本中或在公开可用的网站上注册他们的结果数据，并报告他们的工作结果，无论是在研究文本中还是在公开可访问的数据存储库中。如果主要研究作者没有注册数据收集，同样也没有报告数据收集结果，那么元分析人员就有可能无法纳入已收集的数据。本研究的目的是尝试定位一种元分析数据：既未注册也未报告所收集结果数据的研究的结果。为此，我们进行了大规模的潜在研究搜索，并向 600 多名主要研究作者发送了作者查询请求，询问他们是否收集了合格的结果数据。我们收到了 75 位作者（12.3%）的回复，其中有三位作者发送了合格的发现。搜索结果证实了我们的概念验证（即，作者收集数据但未能公开注册或报告数据），并且元分析结果表明，排除已确定的研究将改变我们的一些实质性结论。然而，成本分析表明，找到缺失的研究代价高昂。最后，我们再次呼吁更多地采用主要研究预先注册以及在公开可用的存储库中归档数据。
