Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence 1st Edition by Elena Kulinskaya (PDF)

Description

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence. The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

User’s Reviews

Editorial Reviews: Review “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) From the Inside Flap 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. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐As this book is in paperback ti sells at a bargain price and is certainly worth it. The authors are expert statistician and experienced writers. They have divided it into two parts. PartI shows the methods and applications and provides nice examples. Part II covers the theory that underlies the methods. Chapter 1 is an overview chapter that gives the reader a sense of what is to come in Part I. The reader will get the key ideas simply by reading chapter 1. Chapters 2-15 do a comprehensive job of covering the various statistical problems and methods and privide ways for combining analyses from separate studies into one composite analysis. The authors make the point that although the p-value is a useful tool fro describing degree to which the “null” hypothesis is disbelieved it is not the most effective measure for combining information. Chapters 2-5 deal with various analyses for continuous data while 6-9 deal with discrete data (often integer data such as in the number of successes in n trials, Binomial model or number of events per unit time, Poisson Model). Unique to meta-analysis is the problem of publication bias which is one of the major issues associated with selecting studies to combine. This issue is covered in Chapter 15. The rest of the book presents the theory which is at the level of a graduate course in statistics. Those not equipped to learn the theory can stop with Chapter 15 and will know how to do most meta-analyses. It also is a convenient placed to stop for anyone who wants to be a practitioner but only needs the theory for occasional reference.

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