Data extraction errors in meta-analyses that use standardized mean differences

Abstract

Context: Meta-analysis of trials that have used different continuous or rating scales to record outcomes of a similar nature requires sophisticated data handling and data transformation to a uniform scale, the standardized mean difference (SMD). It is not known how reliable such meta-analyses are.

Objective: To study whether SMDs in meta-analyses are accurate.

Data sources: Systematic review of meta-analyses published in 2004 that reported a result as an SMD, with no language restrictions. Two trials were randomly selected from each meta-analysis. We attempted to replicate the results in each meta-analysis by independently calculating SMD using Hedges adjusted g.

Data extraction: Our primary outcome was the proportion of meta-analyses for which our result differed from that of the authors by 0.1 or more, either for the point estimate or for its confidence interval, for at least 1 of the 2 selected trials. We chose 0.1 as cut point because many commonly used treatments have an effect of 0.1 to 0.5, compared with placebo.

Results: Of the 27 meta-analyses included in this study, we could not replicate the result for at least 1 of the 2 trials within 0.1 in 10 of the meta-analyses (37%), and in 4 cases, the discrepancy was 0.6 or more for the point estimate. Common problems were erroneous number of patients, means, standard deviations, and sign for the effect estimate. In total, 17 meta-analyses (63%) had errors for at least 1 of the 2 trials examined. For the 10 meta-analyses with errors of at least 0.1, we checked the data from all the trials and conducted our own meta-analysis, using the authors' methods. Seven of these 10 meta-analyses were erroneous (70%); 1 was subsequently retracted, and in 2 a significant difference disappeared or appeared.

Conclusions: The high proportion of meta-analyses based on SMDs that show errors indicates that although the statistical process is ostensibly simple, data extraction is particularly liable to errors that can negate or even reverse the findings of the study. This has implications for researchers and implies that all readers, including journal reviewers and policy makers, should approach such meta-analyses with caution.