Nordic Cochrane Centre, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen Ø, Denmark.
BMJ. 2011 Aug 30;343:d4829. doi: 10.1136/bmj.d4829.
To examine the extent of multiplicity of data in trial reports and to assess the impact of multiplicity on meta-analysis results.
Empirical study on a cohort of Cochrane systematic reviews.
All Cochrane systematic reviews published from issue 3 in 2006 to issue 2 in 2007 that presented a result as a standardised mean difference (SMD). We retrieved trial reports contributing to the first SMD result in each review, and downloaded review protocols. We used these SMDs to identify a specific outcome for each meta-analysis from its protocol. Review methods Reviews were eligible if SMD results were based on two to ten randomised trials and if protocols described the outcome. We excluded reviews if they only presented results of subgroup analyses. Based on review protocols and index outcomes, two observers independently extracted the data necessary to calculate SMDs from the original trial reports for any intervention group, time point, or outcome measure compatible with the protocol. From the extracted data, we used Monte Carlo simulations to calculate all possible SMDs for every meta-analysis.
We identified 19 eligible meta-analyses (including 83 trials). Published review protocols often lacked information about which data to choose. Twenty-four (29%) trials reported data for multiple intervention groups, 30 (36%) reported data for multiple time points, and 29 (35%) reported the index outcome measured on multiple scales. In 18 meta-analyses, we found multiplicity of data in at least one trial report; the median difference between the smallest and largest SMD results within a meta-analysis was 0.40 standard deviation units (range 0.04 to 0.91).
Multiplicity of data can affect the findings of systematic reviews and meta-analyses. To reduce the risk of bias, reviews and meta-analyses should comply with prespecified protocols that clearly identify time points, intervention groups, and scales of interest.
考察试验报告中数据多重性的程度，并评估其对荟萃分析结果的影响。
对科克伦系统评价队列进行的实证研究。
2006 年第 3 期至 2007 年第 2 期发表的所有科克伦系统评价，这些评价均以标准化均数差（SMD）呈现结果。我们检索了为各评价中的第一个 SMD 结果提供试验报告，并下载了评价方案。我们使用这些 SMD 从方案中识别每个荟萃分析的特定结局。
如果 SMD 结果基于 2 至 10 个随机试验，且方案中描述了结局，那么评价即为合格。如果仅呈现了亚组分析的结果，则排除该评价。基于评价方案和主要结局，两位观察者独立从原始试验报告中提取出与方案一致的任何干预组、时间点或结局测量的 SMD 计算所需数据。从提取的数据中，我们使用蒙特卡罗模拟计算了每个荟萃分析的所有可能的 SMD。
我们确定了 19 项合格的荟萃分析（包括 83 项试验）。发表的评价方案往往缺乏有关选择何种数据的信息。24 项（29%）试验报告了多个干预组的数据，30 项（36%）报告了多个时间点的数据，29 项（35%）报告了在多个尺度上测量的主要结局。在 18 项荟萃分析中，我们在至少一份试验报告中发现了数据多重性；荟萃分析内最小和最大 SMD 结果之间的中位数差异为 0.40 个标准差单位（范围 0.04 至 0.91）。
数据多重性可能影响系统评价和荟萃分析的结果。为降低偏倚风险，评价和荟萃分析应符合明确识别感兴趣的时间点、干预组和尺度的预设方案。
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