Institute for Research in Operative Medicine (IFOM), Faculty of Health - School of Medicine, Witten/Herdecke University, Ostmerheimer Str. 200, 51109, Cologne, Germany.
BMC Med Res Methodol. 2020 Oct 19;20(1):259. doi: 10.1186/s12874-020-01143-3.
Data extraction forms link systematic reviews with primary research and provide the foundation for appraising, analysing, summarising and interpreting a body of evidence. This makes their development, pilot testing and use a crucial part of the systematic reviews process. Several studies have shown that data extraction errors are frequent in systematic reviews, especially regarding outcome data.
We reviewed guidance on the development and pilot testing of data extraction forms and the data extraction process. We reviewed four types of sources: 1) methodological handbooks of systematic review organisations (SRO); 2) textbooks on conducting systematic reviews; 3) method documents from health technology assessment (HTA) agencies and 4) journal articles. HTA documents were retrieved in February 2019 and database searches conducted in December 2019. One author extracted the recommendations and a second author checked them for accuracy. Results are presented descriptively.
Our analysis includes recommendations from 25 documents: 4 SRO handbooks, 11 textbooks, 5 HTA method documents and 5 journal articles. Across these sources the most common recommendations on form development are to use customized or adapted standardised extraction forms (14/25); provide detailed instructions on their use (10/25); ensure clear and consistent coding and response options (9/25); plan in advance which data are needed (9/25); obtain additional data if required (8/25); and link multiple reports of the same study (8/25). The most frequent recommendations on piloting extractions forms are that forms should be piloted on a sample of studies (18/25); and that data extractors should be trained in the use of the forms (7/25). The most frequent recommendations on data extraction are that extraction should be conducted by at least two people (17/25); that independent parallel extraction should be used (11/25); and that procedures to resolve disagreements between data extractors should be in place (14/25).
Overall, our results suggest a lack of comprehensiveness of recommendations. This may be particularly problematic for less experienced reviewers. Limitations of our method are the scoping nature of the review and that we did not analyse internal documents of health technology agencies.
数据提取表格将系统评价与原始研究联系起来，并为评估、分析、总结和解释证据提供了基础。这使得数据提取表格的开发、试点测试和使用成为系统评价过程的关键部分。有几项研究表明，数据提取错误在系统评价中很常见，尤其是在结局数据方面。
我们回顾了数据提取表格的开发和试点测试以及数据提取过程的指导。我们查阅了四类来源的资料：1）系统评价组织（SRO）的方法手册；2）系统评价教材；3）卫生技术评估（HTA）机构的方法文件；4）期刊文章。HTA 文件于 2019 年 2 月检索，数据库于 2019 年 12 月检索。一位作者提取建议，另一位作者检查准确性。结果以描述性方式呈现。
我们的分析包括 25 份文件的建议：4 份 SRO 手册、11 本教材、5 份 HTA 方法文件和 5 篇期刊文章。在这些来源中，关于表格开发的最常见建议是使用定制或改编的标准化提取表格（14/25）；提供关于使用表格的详细说明（10/25）；确保清晰和一致的编码和应答选项（9/25）；提前计划需要哪些数据（9/25）；如有需要，获取额外的数据（8/25）；链接同一研究的多个报告（8/25）。关于试点提取表格的最常见建议是，应在研究样本上试点表格（18/25）；数据提取人员应接受表格使用的培训（7/25）。关于数据提取的最常见建议是，提取应由至少两个人进行（17/25）；应使用独立的平行提取（11/25）；应制定解决数据提取人员之间分歧的程序（14/25）。
总体而言，我们的结果表明建议不够全面。这对于经验较少的评价者来说可能是一个特别大的问题。我们方法的局限性在于综述的范围性质，以及我们没有分析卫生技术机构的内部文件。
