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Applied Meta-Analysis with R and Stata
SPAR
kr 63

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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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.

Produktdetaljer
Sprog: Engelsk
Sider: 424
ISBN-13: 9780367709341
Indbinding: Paperback
Udgave:
ISBN-10: 0367709341
Kategori: Kliniske forsøg
Udg. Dato: 26 sep 2022
Længde: 29mm
Bredde: 156mm
Højde: 235mm
Oplagsdato: 26 sep 2022
Forfatter(e) Karl E. (Georgia Southern University Peace, Ding-Geng (Din) (University of North Carolina Chen, Ding-Geng (Din) Chen, Karl E. Peace, Ding-Geng (University of North Carolina Chen


Kategori Kliniske forsøg


Sprog Engelsk


Indbinding Paperback


Sider 424


Udgave


Længde 29mm


Bredde 156mm


Højde 235mm


Udg. Dato 26 sep 2022


Oplagsdato 26 sep 2022

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