Applied Meta-Analysis with R and Stata
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|No. of pages:||456 pages|
|Illustrations:||63 Illustrations, black & white; 23 Tables, black & white|
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.
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.
"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
2. Research Protocol for Meta
-E ects and Random
-E ects in Meta
-Analysis with Binary Data
-Analysis for Continuous Data
6. Heterogeneity in Meta
8. Multivariate Meta
9. Publication Bias in Meta
10. Strategies to Handle Missing Data in Meta
-Analysis for Evaluating Diagnostic Accuracy
12. Network Meta
-Analysis for Rare Events
-Analyses with Individual Patient
-Level Data versus Summary Statistics
15. Other R/Stata Packages for Meta