(Download) "Applied Meta-Analysis with R and Stata" by Ding-Geng (Din) Chen & Karl E. Peace * eBook PDF Kindle ePub Free
eBook details
- Title: Applied Meta-Analysis with R and Stata
- Author : Ding-Geng (Din) Chen & Karl E. Peace
- Release Date : January 30, 2021
- Genre: Mathematics,Books,Science & Nature,Professional & Technical,Medical,Pharmacology & Toxicology,
- Pages : * pages
- Size : 12564 KB
Description
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.