Carroll Christopher, Scope Alison, Kaltenthaler Eva
Health Economics and Decision Science (HEDS), School of Health and Related Research (ScHARR), Regent court, Regent Street, Sheffield S1 4DA, UK.
BMC Res Notes. 2013 Dec 17;6:539. doi: 10.1186/1756-0500-6-539.
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.
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.
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.
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.
数据提取是系统评价中的关键阶段，但相关研究较少。本研究旨在通过一个小型案例研究，突出影响这一基础过程的一些重要问题。
作者对三项关于同一主题（全髋关节置换术与半髋关节置换术比较）的系统评价所提取和分析的二元事件数据差异进行了分析。从这三项评价共有的试验中，提取了以下三个关键结局的二元事件数据：脱位率、1年死亡率和翻修率。将三项评价提取的数据差异归类为错误或数据选择问题。进行荟萃分析以评估这些差异是否导致效应汇总估计值的差异。
在这三个结局中，选择差异占评价间数据差异的8%至42%，错误占8%至17%。在这些情况中，均未给出报告事件数据选择的理由。这些差异确实导致三项评价中两种治疗方法的荟萃分析相对风险存在微小差异，但均无统计学意义。
系统评价者应采用双重数据提取以尽量减少错误，并在其发表范围内尽一切努力阐明或解释其数据选择。当面对一个结局有多种但可能同样合适的数据可供选择时，评价者经常会进行选择。然而，评价作者很少明确说明这种选择。系统评价作为一种方法，其目的是具有可重复性和透明度。本案例研究表明，这两个目标并非总能实现。
