A case study of binary outcome data extraction across three systematic reviews of hip arthroplasty: errors and differences of selection.

BACKGROUND: Data extraction is a key stage in systematic review, yet it is the subject of little research. The aim of the present research was to use a small case study to highlight some important issues affecting this fundamental process. METHODS: The authors undertook an analysis of differences in the binary event data extracted and analysed by three systematic reviews on the same topic: a comparison of total hip arthroplasty and hemiarthroplasty. The following binary event data were extracted for three key outcomes, common to all three reviews, from those trials common to all three reviews: Dislocation rates, 1-year mortality, and revision rates. Differences between the data extracted by the three reviews were categorised as either errors or an issue of data selection. Meta-analysis was performed to assess whether these differences led to differences in summary estimates of effect. RESULTS: Across the three outcomes, differences in selection accounted for between 8% and 42% of the data differences between reviews, and errors accounted for between 8% and 17%. No rationale was given in any of these former cases for the choice of event data being reported. These differences did lead to small differences in meta-analysed relative risks between the two treatments in the three reviews, but none was significant. CONCLUSIONS: Systematic reviewers should use double-data extraction to minimise error and also make every effort to clarify or explain their choice of data, within the scope of their publication. Reviewers frequently exercise selection when faced with a choice of alternative but potentially equally appropriate data for an outcome. However, this selection is rarely made clear by review authors. Systematic review was developed as a method specifically to be both reproducible and transparent. This case study suggests that neither objective is always being achieved