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Review of the First Edition:


The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis... A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs.


-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.





What's New in the Second Edition:








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.

Applied Meta-Analysis with R and Stata

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

Hani Samawi

10. Strategies to Handle Missing Data in Meta-Analysis

Haresh Rochani and Mario Keko

11. Meta-Analysis for Evaluating Diagnostic Accuracy

Jingjing Yin and Jing Kersey

12. Network Meta-Analysis

Lili Yu and Xinyan Zhang

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