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.

"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)

• Preface xiiiPart I The Methods 11 What can the reader expect from this book? 32 Independent measurements with known precision 153 Independent measurements with unknown precision 234 Comparing treatment to control 315 Comparing K treatments 396 Evaluating risks 477 Comparing risks 518 Evaluating Poisson rates 579 Comparing Poisson rates 6310 Goodness-of-fit testing 7111 Evidence for heterogeneity of effects and transformed effects 7712 Combining evidence: fixed standardized effects model 8513 Combining evidence: random standardized effects model 9114 Meta-regression 9515 Accounting for publication bias 105Part II The Theory 11116 Calibrating evidence in a test 11317 The basics of variance stabilizing transformations 12518 One-sample binomial tests 13919 Two-sample binomial tests 14920 Defining evidence in t-statistics 15921 Two-sample comparisons 17322 Evidence in the chi-squared statistic 18123 Evidence in F-tests 19324 Evidence in Cochran's Q for heterogeneity of effects 20725 Combining evidence from K studies 21926 Correcting for publication bias 23127 Large-sample properties of variance stabilizing transformations 239References 249Index 253