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Nature Research Academies – Systematic Reviews and Meta-Analyses
Nature Research Academies – Systematic Reviews and Meta-Analyses

Nature Research Academies – Systematic Reviews and Meta-Analyses

Available from Nature Research Academies: workshops in Systematic Reviews and Meta-Analyses. Nature Research Academies is a series of training workshops for researchers, developed by Nature Research.

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Academy in Systematic Reviews and Meta-Analyses

Systematic reviews and meta-analyses are crucial in synthesizing clinical research to determine the efficacy of an intervention. However, conducting high-quality systematic reviews and meta-analyses that have clinical impact on the field takes time and expertise.

Nature Research Academies has developed this workshop in ‘Systematic Reviews and Meta-Analyses’ for clinical researchers, to explain how to conduct a systematic review or a meta-analysis.

  • 1-day workshops
  • Available to institutions globally, to host on-site
  • For up to 50 clinical researchers

Feedback from participants

“Your workshop was very helpful for all Chinese students. I previously wrote a meta-analysis with my colleague, but it was rejected. I never understood why we failed until you made such a wonderful speech! Thank you so much!”

Academy content

  • Types of reviews
    • Differences between literature review, systematic review and meta-analysis
    • Strengths and weaknesses of systematic reviews and meta-analysis
    • Ethical consideration: prior registration
  • Searching and selecting studies
    • Searching the white and grey literature
    • The importance of exploring multiple databases
    • Deciding the inclusion and exclusion criteria
    • Duplicate publications
  • Extracting data
    • Using a standardized format
    • Effect sizes and precision of estimates
  • Presenting data
    • Summarizing data using a forest plot
    • Estimating publication bias using a funnel plot
    • Alternatives to forest plot and funnel plot
  • Analyzing data
    • Random effect vs. fixed effect analysis models
    • Evaluating heterogeneity between studies
    • Rating the quality of evidence

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