Frequency of data extraction errors and methods to increase data extraction quality: A methodological review

Abstract

Background: Our objective was to assess the frequency of data extraction errors and its potential impact on results in systematic reviews. Furthermore, we evaluated the effect of different extraction methods, reviewer characteristics and reviewer training on error rates and results. Methods: We performed a systematic review of methodological literature in PubMed, Cochrane methodological registry, and by manual searches (12/2016). Studies were selected by two reviewers independently. Data were extracted in standardized tables by one reviewer and verified by a second. Results: The analysis included six studies; four studies on extraction error frequency, one study comparing different reviewer extraction methods and two studies comparing different reviewer characteristics. We did not find a study on reviewer training. There was a high rate of extraction errors (up to 50%). Errors often had an influence on effect estimates. Different data extraction methods and reviewer characteristics had moderate effect on extraction error rates and effect estimates. Conclusion: The evidence base for established standards of data extraction seems weak despite the high prevalence of extraction errors. More comparative studies are needed to get deeper insights into the influence of different extraction methods.