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Meta-Analysis with R (Use R!), by Guido Schwarzer, James R. Carpenter, Gerta R�cker
PDF Download Meta-Analysis with R (Use R!), by Guido Schwarzer, James R. Carpenter, Gerta R�cker
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This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.
- Sales Rank: #158528 in Books
- Brand: Springer
- Published on: 2015-10-10
- Released on: 2015-11-09
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .62" w x 6.10" l, .0 pounds
- Binding: Paperback
- 252 pages
- Springer
Review
“A special feature of the book is the focus on comprehensively described examples. For all these examples, the datasets are provided on the book-related website, which allows readers to reproduce and check the R code while working on the book. … it can be warmly recommended to all practical researchers who are involved in performing metaanalysis in R as well as to statisticians who seek a book that gives an overview on metaanalytic methods and their implementations in R.” (Annika Hoyer, Biometrical Journal, Vol. 59 (1), 2017)
“In Meta-Analysis with R, Schwarzer, Carptenter, and Rücker present the tools and techniques for doing meta-analyses in R. … The collection of working examples throughout the book is its best feature. Fully worked code examples are provided for almost every problem. … The book is a great introduction to performing meta-analysis in R.” (James P. Howard II, Journal of Statistical Software, Vol. 70, April, 2016)
“The book concludes with an appendix containing information on how to install R, how to import data (either from text files or from RevMan5) and an overview of R packages for meta analysis. The style of the book, numerous example and references adjacent to each chapter make it suitable (and very useful) to both undergraduates and postgraduates with either a computing or biological background.” (Irina Ioana Mohorianu, zbMATH 1333.92002, 2016)
From the Back Cover
This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.
About the Author
Guido Schwarzer is a senior statistician and head of IT at the Institute for Medical Biometry and Statistics at the Medical Center - University of Freiburg, Germany. He is an established researcher in the area of meta-analysis and lead statistician of several Cochrane reviews. His special interests are in small-study effects in meta-analysis and statistical computing. Guido Schwarzer is an author of the R packages meta, metasens, and netmeta.
James Carpenter studied mathematics at Warwick University and statistics at Oxford University. His principal interests are coping with missing data in complex hierarchical models, sensitivity analysis and meta-analysis, with applications to medical and social data. The collaboration that led to this book began during a sabbatical at Freiburg in 2005–6. James Carpenter is Professor of Medical Statistics at the London School of Hygiene and Tropical Medicine, and Programme Leader in Methodology at the MRC Clinical Trials Unit, London.
Gerta Rücker is a mathematician, working at the Institute for Medical Biometry and Statistics at the Medical Center - University of Freiburg, Germany. After having published in chemical graph theory for a number of years, she started working in biostatistics, particularly meta-analysis. Her principal interests are small-study effects and heterogeneity in meta-analysis, meta-analysis of diagnostic accuracy studies and application of graph theory in network meta-analysis. She has published a large number of methodological research papers, co-authored a number of Cochrane reviews and is an author of the R package netmeta.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Practical and concise introduction to useful tools to make meta-analysis with R.
By Sergio Uribe
This book cover mainly the meta package. Gives the mathematical approach and then give you the commands to make those complicated formulas with simple code in R using the meta package. As a clinician this book has been absolutely useful to perform a full meta-analysis. The first part introduces the reader to R. The second part cover the standards methods for met-analysis (continuos, binary outcomes) and the third part includes small-study meta-analysis, dealing with missing data, multivariate and network meta-analysis, and diagnostic meta-analysis.
No information about graphical representation of results is included.
Recommended for anyone interested in perform a meta-analysis with the meta package or manually. Also of interest is the metafor package.
UPDATE
Dec 2016. After having read it and use a lot, I found this book practical to start and do a meta-analisis. Still I miss some information about how to report (graph and personlized tables), but overall I found every day more useful. One star +.
0 of 0 people found the following review helpful.
Came with perfectly packed
By Amy Lin
Came with described, perfectly packed and brand new !
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