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.
