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Applied meta-analysis with R and stata / Ding-Geng (Din) Chen, Karl E. Peace.

By: Contributor(s): Material type: TextTextLanguage: English Series: Chapman and Hall/ CRC Biostatistics SeriesPublication details: Boca Raton : CRC Press, 2021.Edition: 2nd EditionDescription: xxxii, 424 p.; 21 cm PBISBN:
  • 9780367709341
Subject(s): DDC classification:
  • 005.73 CHE
Contents:
1. Introduction to R and Stata for Meta-Analysis2. Research Protocol for Meta-Analyses3. Fixed-E ects and Random-E ects in Meta-Analysis4. Meta-Analysis with Binary Data5. Meta-Analysis for Continuous Data6. Heterogeneity in Meta-Analysis7. Meta-Regression8. Multivariate Meta-Analysis9. Publication Bias in Meta-Analysis10. Strategies to Handle Missing Data in Meta-Analysis11. Meta-Analysis for Evaluating Diagnostic Accuracy12. Network Meta-Analysis13. Meta-Analysis for Rare Events14. Meta-Analyses with Individual Patient-Level Data versus Summary Statistics15. Other R/Stata Packages for Meta-Analysis
Summary: In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, this book shows how to implement statistical meta-analysis methods to real data using R and Stata.
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Book Book Ranganathan Library Mathematics and Statistics 005.73 CHE (Browse shelf(Opens below)) Available 050103


1. Introduction to R and Stata for Meta-Analysis2. Research Protocol for Meta-Analyses3. Fixed-E ects and Random-E ects in Meta-Analysis4. Meta-Analysis with Binary Data5. Meta-Analysis for Continuous Data6. Heterogeneity in Meta-Analysis7. Meta-Regression8. Multivariate Meta-Analysis9. Publication Bias in Meta-Analysis10. Strategies to Handle Missing Data in Meta-Analysis11. Meta-Analysis for Evaluating Diagnostic Accuracy12. Network Meta-Analysis13. Meta-Analysis for Rare Events14. Meta-Analyses with Individual Patient-Level Data versus Summary Statistics15. Other R/Stata Packages for Meta-Analysis

In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, this book shows how to implement statistical meta-analysis methods to real data using R and Stata.

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