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The methodological quality of meta-analyses indexed in PsycINFO: leads for enhancements – a meta-epidemiological study

Authors
  • Leclercq, Victoria
  • Beaudart, Charlotte
  • Ajamieh, Sara
  • Tirelli, Ezio
  • Bruyère, Olivier
Publication Date
Jan 01, 2020
Source
ORBi
Keywords
Language
English
License
Green
External links

Abstract

Objectives Meta-analyses (MAs) are often used because they are lauded to provide robust evidence that synthesises information from multiple studies. However, the validity of MA conclusions relies on the procedural rigour applied by the authors. Therefore, this meta-research study aims to characterise the methodological quality and meta-analytic practices of MAs indexed in PsycINFO. Design A meta-epidemiological study. Participants We evaluated a random sample of 206 MAs indexed in the PsycINFO database in 2016. Primary and secondary outcomes Two authors independently extracted the methodological characteristics of all MAs and checked their quality according to the 16 items of the A MeaSurement Tool to Assess systematic Reviews (AMSTAR2) tool for MA critical appraisal. Moreover, we investigated the effect of mentioning Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) on the methodological quality of MAs. Results According to AMSTAR2 criteria, 95% of the 206 MAs were rated as critically low quality. Statistical methods were appropriate and publication bias was well evaluated in 87% and 70% of the MAs, respectively. However, much improvement is needed in data collection and analysis: only 11% of MAs published a research protocol, 44% had a comprehensive literature search strategy, 37% assessed and 29% interpreted the risk of bias in the individual included studies, and 11% presented a list of excluded studies. Interestingly, the explicit mentioning of PRISMA suggested a positive influence on the methodological quality of MAs. Conclusion The methodological quality of MAs in our sample was critically low according to the AMSTAR2 criteria. Some efforts to tremendously improve the methodological quality of MAs could increase their robustness and reliability.

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