Meta-Analysis with R (Use R!) - Erstausgabe

EAN (ISBN-13): 9783319214153
ISBN (ISBN-10): 3319214152
Taschenbuch
Erscheinungsjahr: 2015
Herausgeber: Springer

Buch in der Datenbank seit 2015-11-08T18:58:34+01:00 (Berlin)
Detailseite zuletzt geändert am 2024-04-16T12:10:19+02:00 (Berlin)
ISBN/EAN: 9783319214153

ISBN - alternative Schreibweisen:
3-319-21415-2, 978-3-319-21415-3
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: schwarze, schwarzer, guido, schwarz, carpenter, rücker
Titel des Buches: gerta, meta analysis with

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Daten vom Verlag:

Autor/in: Guido Schwarzer; James R. Carpenter; Gerta Rücker
Titel: Use R! Meta-Analysis with R
Verlag: Springer; Springer International Publishing
252 Seiten
Erscheinungsjahr: 2015-10-16
Cham; CH
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
64,19 € (DE)
65,99 € (AT)
71,00 CHF (CH)
POD
XII, 252 p. 58 illus., 4 illus. in color.

BC; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; diagnostic studies; meta-analysis; missing data; multivariate analysis; small study effects; Biostatistics; Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy; Sozialforschung und -statistik; EA

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.

 

Part I Getting Started: An Introduction to Meta-Analysis in R.- Part II Standard Methods: Fixed Effect and Random Effects Meta-Analysis.- Meta-Analysis with Binary Outcomes.- Heterogeneity and Meta-Regression.- Part III Advanced Topics: Small-Study Effects in Meta-Analysis.- Missing Data in Meta-Analysis.- Multivariate Meta-Analysis.- Network Meta-Analysis.- Meta-Analysis of Diagnostic Test Accuracy Studies.- Further Information on R.- Index.

“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 performingmeta-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)

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.

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.

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.

Guido Schwarzer James Carpenter Gerta Rücker

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.

 

Includes step-by-step tutorials to help the reader to understand and apply meta-analytical methods Takes readers through all the steps involved in preparing graphical summaries of results, using flexible software written by the authors Features extensive examples that strike an excellent balance between the how-to of the R code, the statistical methods involved, and discussion of the data and interpretation of the results 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 Is supported by a supplementary web-resource at www:meta-analysis-with-r.org Includes supplementary material: sn.pub/extras