Short description:

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.

Long description:

Review of the First Edition:

?Journal of Applied Statistics

Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions.

• Adds Stata programs along with the R programs for meta-analysis








• Updates all the statistical meta-analyses with R/Stata programs








• Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS








• Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA

Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

"The strengths of the second edition continue those of the first edition... A summary and discussion close the chapters, providing professionally generous recommendations for additional reading, software, and websites. Clearly, an applied hands-on approach intended to facilitate quickly moving readers to performing informed meta-data analyses."
- Thomas E. Bradstreet, Journal of Biopharmaceutical Statistics, July 2022

Table of Contents:

1. Introduction to R and Stata for Meta-Analysis
2. Research Protocol for Meta-Analyses
3. Fixed-E ects and Random-E ects in Meta-Analysis
4. Meta-Analysis with Binary Data
5. Meta-Analysis for Continuous Data
6. Heterogeneity in Meta-Analysis
7. Meta-Regression
8. Multivariate Meta-Analysis
9. Publication Bias in Meta-Analysis
10. Strategies to Handle Missing Data in Meta-Analysis
11. Meta-Analysis for Evaluating Diagnostic Accuracy
12. Network Meta-Analysis
13. Meta-Analysis for Rare Events
14. Meta-Analyses with Individual Patient-Level Data versus Summary Statistics
15. Other R/Stata Packages for Meta-Analysis