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Area of Science:

  • Biostatistics
  • Psychological Research Methods
  • Evidence-Based Medicine

Background:

  • Meta-analysis is crucial for unbiased evidence assessment, with increasing use in mental health research.
  • Understanding meta-analysis methodology is essential for accurate interpretation of research findings.
  • The R statistical environment offers powerful tools for conducting meta-analyses.

Purpose of the Study:

  • To provide a practical guide on performing meta-analyses using the R statistical software.
  • To illustrate meta-analysis techniques with a real-world example from mental health research.
  • To familiarize researchers with essential R commands for meta-analysis and sensitivity analyses.

Main Methods:

  • Utilized the 'meta' R package for standard meta-analysis procedures.
  • Employed the 'metasens' R package for sensitivity analyses concerning missing data and selection bias.
  • Provided clear descriptions and essential R commands for all analytical steps.

Main Results:

  • Demonstrated fixed-effect and random-effects meta-analysis for binary outcomes.
  • Illustrated the creation of forest plots, funnel plots, and methods for testing/adjusting funnel plot asymmetry.
  • Confirmed that these methods are adaptable to various outcome types.

Conclusions:

  • R is a versatile and potent software environment for conducting meta-analyses.
  • This publication offers foundational knowledge and directs users to advanced meta-analysis techniques within R.
  • Researchers can effectively synthesize evidence using R for robust mental health studies.