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Statistical Meta-Analysis with Applications
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Statistical Meta-Analysis with Applications Hardcover - 2008 - 1st Edition

by Joachim Hartung; Guido Knapp; Bimal K. Sinha


From the publisher

Mehr als zehnjhrige Erfahrung in der Verbesserung von Methoden zur Meta-Analyse flie en in dieses Werk ein, wobei vollkommen neue Aspekte und Mglichkeiten zur Verbindung einzelner Verfahren aufgezeigt werden. Auch die elektronischen Verarbeitungsmglichkeiten von Daten hierzu wird besprochen.

From the rear cover

An accessible introduction to performing meta-analysis across various areas of research

The practice of meta-analysis allows researchers to obtain findings from various studies and compile them to verify and form one overall conclusion. Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies.

Two main types of statistical analysis serve as the foundation of the methods and techniques: combining tests of effect size and combining estimates of effect size. Additional topics covered include:

  • Meta-analysis regression procedures
  • Multiple-endpoint and multiple-treatment studies

  • The Bayesian approach to meta-analysis

  • Publication bias

  • Vote counting procedures

  • Methods for combining individual tests and combining individual estimates

  • Using meta-analysis to analyze binary and ordinal categorical data

Numerous worked-out examples in each chapter provide the reader with a step-by-step understanding of the presented methods. All exercises can be computed using the R and SAS(R) software packages, which are both available via the book's related Web site. Extensive references are also included, outlining additional sources for further study.

Requiring only a working knowledge of statistics, Statistical Meta-Analysis with Applications is a valuable supplement for courses in biostatistics, business, public health, and social research at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians working in industry, academia, and government.

Details

  • Title Statistical Meta-Analysis with Applications
  • Author Joachim Hartung; Guido Knapp; Bimal K. Sinha
  • Binding Hardcover
  • Edition number 1st
  • Edition 1
  • Pages 272
  • Volumes 1
  • Language ENG
  • Publisher Wiley-Interscience
  • Date 2008-08-01
  • Illustrated Yes
  • ISBN 9780470290897 / 0470290897
  • Weight 1.1 lbs (0.50 kg)
  • Dimensions 9.3 x 6.3 x 0.7 in (23.62 x 16.00 x 1.78 cm)
  • Library of Congress subjects Data Interpretation, Statistical, Statistical hypothesis testing
  • Library of Congress Catalog Number 2008009435
  • Dewey Decimal Code 519.56

Media reviews

Citations

  • Choice, 04/01/2009, Page 0
  • Scitech Book News, 12/01/2008, Page 37

About the author

JOACHIM HARTUNG, PhD, is Professor in the Department of Statistics at the Dortmund University of Technology, Germany. He has published several books and two dozen journal articles in the field of statistics.

GUIDO KNAPP, PhD, is Assistant Professor in the Department of Statistics at the Dortmund University of Technology, Germany. Dr. Knapp's areas of research interest include variance component models, error components regression models, meta-analysis, and flexible design in clinical trials.

BIMAL K. SINHA, PhD, is Presidential Research Professor of Statistics in the Department of Mathematics and Statistics at the University of Maryland at Baltimore County (UMBC). A Fellow of both the Institute of Mathematical Statistics and the American Statistical Association, Dr. Sinha's research specializes in the areas of multivariate analysis, mixed linear models, decision theory, robustness, and environmental statistics.

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