Applied Meta-analysis with R and Stata
Applied Meta-analysis with R and Stata by Ding-Geng (Din) Chen, published by CRC Press in 2021, is a comprehensive guide designed for practitioners and researchers interested in statistical meta-analysis. This second edition spans 424 pages and is presented in English. The book emphasizes practical applications over theoretical concepts, making it accessible for individuals in fields such as biology and medicine who may not have an advanced mathematics background. It introduces readers to meta-analysis methods through step-by-step implementations using R and Stata, focusing on real-world examples from existing literature.
Readers will find detailed instructions for analyzing meta-data, including fixed-effects and random-effects meta-analyses, meta-regression, and methods for handling publication bias and missing data. The edition also includes new chapters on multivariate meta-analysis and network meta-analysis, enhancing its utility as both a graduate-level text and a reference for biostatisticians and public health professionals. By providing practical coding examples and relevant applications, this book serves as a valuable resource for those looking to familiarize themselves with meta-analysis techniques and software.
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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.
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