Meta Analysis - Elena Kulinskaya

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Résumé :
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....

Biographie:
Dr. E. Kulinskaya - Director, Statistical Advisory Service, Imperial College, London.

Professor S. Morgenthaler - Chair of Applied Statistics, Ecole Polytechnique F?d?rale 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....

Sommaire:

Preface xiii

Part I The Methods 1

1 What can the reader expect from this book? 3
2 Independent measurements with known precision 15
3 Independent measurements with unknown precision 23
4 Comparing treatment to control 31
5 Comparing K treatments 39
6 Evaluating risks 47
7 Comparing risks 51
8 Evaluating Poisson rates 57
9 Comparing Poisson rates 63
10 Goodness-of-fit testing 71
11 Evidence for heterogeneity of effects and transformed effects 77
12 Combining evidence: fixed standardized effects model 85
13 Combining evidence: random standardized effects model 91
14 Meta-regression 95
15 Accounting for publication bias 105

Part II The Theory 111

16 Calibrating evidence in a test 113
17 The basics of variance stabilizing transformations 125
18 One-sample binomial tests 139
19 Two-sample binomial tests 149
20 Defining evidence in t-statistics 159
21 Two-sample comparisons 173
22 Evidence in the chi-squared statistic 181
23 Evidence in F-tests 193
24 Evidence in Cochran's Q for heterogeneity of effects 207
25 Combining evidence from K studies 219
26 Correcting for publication bias 231
27 Large-sample properties of variance stabilizing transformations 239

References 249
Index 253