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This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.
List of contents
1. Introduction. 2.Discovering R. 3. Effect Sizes. 4. Pooling Effect Sizes. 5. Between-Study Heterogeneity. 6. Forest Plots. 7. Subgroup Analyses. 8. Meta-Regression. 9. Publication Bias. 10. "Multilevel" Meta-Analysis. 11. Structural Equation Modeling Meta-Analysis. 12. Network Meta-Analysis. 13. Bayesian Meta-Analysis. 14. Power Analysis. 15. Risk of Bias Plots. 16. Reporting & Reproducibility. 17. Effect Size Calculation & Conversion.
About the author
Mathias Harrer is a research associate at the Friedrich-Alexander-University Erlangen-Nuremberg. Mathias' research focuses on biostatistical and technological approaches in psychotherapy research, methods for clinical research synthesis, and on the development of statistical software.
Pim Cuijpers is professor of Clinical Psychology at the VU University Amsterdam. He is specialized in conducting randomized controlled trials and meta-analyses, with a focus on the prevention and treatment of common mental disorders. Pim has published more than 800 articles in international peer-reviewed scientific journals; many of which are meta-analyses of clinical trials.
Toshi A. Furukawa is professor of Health Promotion and Human Behavior at the Kyoto University School of Public Health. His seminal research focuses both on theoretical aspects of research synthesis and meta-analysis, as well as their application in evidence-based medicine.
David D. Ebert is professor of Psychology and Behavioral Health Technology at the Technical University of Munich. David's research focuses internet-based intervention, clinical epidemiology, as well as applied research synthesis in this field.
Summary
This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.
