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Everything about Meta-analysis

Statistical method that summarizes and/or integrates data from multiple sources

Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part of this method involves computing a combined effect size across all of the studies. As such, this statistical approach involves extracting effect sizes and variance measures from various studies. By combining these effect sizes the statistical power is improved and can resolve uncertainties or discrepancies found in individual studies. Meta-analyses are integral in supporting research grant proposals, shaping treatment guidelines, and influencing health policies. They are also pivotal in summarizing existing research to guide future studies, thereby cementing their role as a fundamental methodology in metascience. Meta-analyses are often, but not always, important components of a systematic review. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Meta-analysisA 5-minute orientation
  2. Skim a recent paper: MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretationInfluential work since 2022
  3. Pick up a classic book: Introduction to meta-analysisThe most-reprinted book on the subject
  4. Read the landmark paper: Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA StatementCited 59,937 times
  5. Go sideways: Study heterogeneityA closely related topic
  6. Go sideways: Meta-regressionA closely related topic

📖 Definitions

Noun Any systematic procedure for statistically combining the results of many different studies.

Noun An analysis resulting from combining the results of diverse statistical studies.

Noun An analysis performed at a higher level of abstraction than that of basic analysis.

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics