“A book that offers an alternative, widely applicable, rigorously justified theory of meta-analysis.” (Evidence Based Medicine, April 2009)
“The book is well written and includes many examples. The book provides an interesting angle on statistical inference by introducing the concept of ‘evidence’. I enjoyed this concept very much.” (Statistics in Medicine, May 2009)
“I found the book well written, reasonably complete, and easy to read … .I recommend this book for both the new and experienced meta-analysts.” (Journal of Biopharmaceutical Statistics, March 2009)
Studies based on small sample sizes often suffer from low power in detecting effects of interest, but this can be overcome by a meta analysis: the combination and analysis of results from a number of studies. This procedure allows for a more accurate estimation of effects, while taking into account differences between study conditions.
In Meta Analysis the results from different studies are transformed to a common calibration scale, where it is simpler to combine and interpret them. This unique approach, developed by the authors, is applicable to many study designs and conditions, and also leads to a deeper understanding of statistical evidence. The book is presented in two parts: Part 1 illustrates the methods required to combine and interpret statistical evidence, while Part 2 provides the motivation, theory and simulation experiments which justify the methods.
The book:
- Provides a user-friendly guide for readers wishing to combine evidence from different statistical experiments.
- Examines methods of continuous and discrete measurement, and regression, before presenting alternative methods for combining evidence.
- Contains many worked examples throughout.
- Is supported by a website containing examples with software instructions for the R environment.
Meta Analysis is ideally suited for statistical consultants and researchers in the fields of medicine, the social sciences and forensic statistics. Medical professionals undertaking basic training in statistics will also find this guide invaluable, as will practitioners of statistics interested in evidentiary statistics and related topics.
From the Back Cover
Studies based on small sample sizes often suffer from low power in detecting effects of interest, but this can be overcome by a meta analysis: the combination and analysis of results from a number of studies. This procedure allows for a more accurate estimation of effects, while taking into account differences between study conditions.
In Meta Analysis the results from different studies are transformed to a common calibration scale, where it is simpler to combine and interpret them. This unique approach, developed by the authors, is applicable to many study designs and conditions, and also leads to a deeper understanding of statistical evidence. The book is presented in two parts: Part 1 illustrates the methods required to combine and interpret statistical evidence, while Part 2 provides the motivation, theory and simulation experiments which justify the methods.
The book:
- Provides a user-friendly guide for readers wishing to combine evidence from different statistical experiments.
- Examines methods of continuous and discrete measurement, and regression, before presenting alternative methods for combining evidence.
- Contains many worked examples throughout.
- Is supported by a website containing examples with software instructions for the R environment.
Meta Analysis is ideally suited for statistical consultants and researchers in the fields of medicine, the social sciences and forensic statistics. Medical professionals undertaking basic training in statistics will also find this guide invaluable, as will practitioners of statistics interested in evidentiary statistics and related topics.
About the Author
Dr. E. Kulinskaya – Director, Statistical Advisory Service, Imperial College, London.
Professor S. Morgenthaler – Chair of Applied Statistics, Ecole Polytechnique Fédérale de Lausanne, Switzerland. Professor Morgenthaler was Assistant Professor at Yale University prior to moving to EPFL and has chaired various ISI committees.
Professor R. G. Staudte – Department of Statistical Science, La Trobe University, Melbourne. During his career at La Trobe he has served as Head of the Department of Statistical Science for five years and Head of the School of Mathematical and Statistical Sciences for two years. He was an Associate Editor for the Journal of Statistical Planning & Inference for 4 years, and is a member of the American Statistical Association, the Sigma Xi Scientific Research Society and the Statistical Society of Australia.
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