Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
| Author | : | |
| Rating | : | 4.29 (945 Votes) |
| Asin | : | 0470028645 |
| Format Type | : | paperback |
| Number of Pages | : | 282 Pages |
| Publish Date | : | 2013-03-02 |
| Language | : | English |
DESCRIPTION:
"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)
This section allows someone with a rudimentary knowledge in general statistics to apply the methods. 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.. The authors aim to promote a deeper understanding of the notion of statistical evidence. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology. 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. 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
. G. Kulinskaya – Director, Statistical Advisory Service, Imperial College, London.Professor S. 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. Dr. Sta
an authoritative and well-organized guide to meta analysis 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 coverin