Applied Meta-Analysis with R and Stata
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| ISBN13: | 9780367183837 |
| ISBN10: | 0367183838 |
| Binding: | Hardback |
| No. of pages: | 456 pages |
| Size: | 234x156 mm |
| Weight: | 793 g |
| Language: | English |
| Illustrations: | 63 Illustrations, black & white; 23 Tables, black & white |
| 580 |
Probability and mathematical statistics
Biology in general
Medicine in general
Infectious diseases, microbiology
Pharmacy
Chemical engineering and industry
Data management in computer systems
High-level programming
Additional devices
Psychology theory
Probability and mathematical statistics (charity campaign)
Biology in general (charity campaign)
Medicine in general (charity campaign)
Infectious diseases, microbiology (charity campaign)
Pharmacy (charity campaign)
Chemical engineering and industry (charity campaign)
Data management in computer systems (charity campaign)
High-level programming (charity campaign)
Additional devices (charity campaign)
Psychology theory (charity campaign)
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-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
