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Background: Studies have shown that industry-sponsored meta-analyses of drugs lack scientific rigour and have biased conclusions. However, these studies have been restricted to certain medical specialities. We compared all industry-supported meta-analyses of drug-drug comparisons with those without industry support.Methods: We searched PubMed for all meta-analyses that compared different drugs or classes of drugs published in 2004. Two authors assessed the meta-analyses and independently extracted data. We used a validated scale for judging the methodological quality and a binary scale for judging conclusions. We divided the meta-analyses according to the type of support in 3 categories: industry-supported, non-profit support or no support, and undeclared support.Results: We included 39 meta-analyses. Ten had industry support, 18 non-profit or no support, and 11 undeclared support. On a 0-7 scale, the median quality score was 6 for meta-analyses with non-profit or no support and 2.5 for the industry-supported meta-analyses (P < 0.01). Compared with industry-supported meta-analyses, more meta-analyses with non-profit or no support avoided bias in the selection of studies (P = 0.01), more often stated the search methods used to find studies (P = 0.02), searched comprehensively (P < 0.01), reported criteria for assessing the validity of the studies (P = 0.02), used appropriate criteria (P = 0.04), described methods of allocation concealment (P = 0.05), described methods of blinding (P = 0.05), and described excluded patients (P = 0.08) and studies (P = 0.15). Forty percent of the industry-supported meta-analyses recommended the experimental drug without reservations, compared with 22% of the meta-analyses with non-profit or no support (P = 0.57).In a sensitivity analysis, we contacted the authors of the meta-analyses with undeclared support. Eight who replied that they had not received industry funding were added to those with non-profit or no support, and 3 who did not reply were added to those with industry support. This analysis did not change the results much.Conclusion: Transparency is essential for readers to make their own judgment about medical interventions guided by the results of meta-analyses. We found that industry-supported meta-analyses are less transparent than meta-analyses with non-profit support or no support. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]