Detecting, quantifying and adjusting for publication bias in meta-analyses: protocol of a systematic review on methods Journal Articles uri icon

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  • Abstract Background Health professionals and policymakers aspire to make healthcare decisions based on the entire relevant research evidence. This, however, can rarely be achieved because a considerable amount of research findings are not published, especially in case of ‘negative’ results - a phenomenon widely recognized as publication bias. Different methods of detecting, quantifying and adjusting for publication bias in meta-analyses have been described in the literature, such as graphical approaches and formal statistical tests to detect publication bias, and statistical approaches to modify effect sizes to adjust a pooled estimate when the presence of publication bias is suspected. An up-to-date systematic review of the existing methods is lacking. Methods/design The objectives of this systematic review are as follows: • To systematically review methodological articles which focus on non-publication of studies and to describe methods of detecting and/or quantifying and/or adjusting for publication bias in meta-analyses. • To appraise strengths and weaknesses of methods, the resources they require, and the conditions under which the method could be used, based on findings of included studies. We will systematically search Web of Science, Medline, and the Cochrane Library for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for publication bias in meta-analyses. A dedicated data extraction form is developed and pilot-tested. Working in teams of two, we will independently extract relevant information from each eligible article. As this will be a qualitative systematic review, data reporting will involve a descriptive summary. Discussion Results are expected to be publicly available in mid 2013. This systematic review together with the results of other systematic reviews of the OPEN project (To Overcome Failure to Publish Negative Findings) will serve as a basis for the development of future policies and guidelines regarding the assessment and handling of publication bias in meta-analyses.


  • Mueller, Katharina Felicitas
  • Meerpohl, Joerg J
  • Briel, Matthias
  • Antes, Gerd
  • Elm, Erik von
  • Lang, Britta
  • Gloy, Viktoria
  • Motschall, Edith
  • Schwarzer, Guido
  • Bassler, Dirk

publication date

  • December 2013