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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.
Auteur
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.
Texte du rabat
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.
Contenu
Preface. Part I The Methods. 1 What can the reader expect from this book? 2 Independent measurements with known precision. 3 Independent measurements with unknown precision. 4 Comparing treatment to control. 5 Comparing K treatments. 6 Evaluating risks. 7 Comparing risks. 8 Evaluating Poisson rates. 9 Comparing Poisson rates. 10 Goodness-of-fit testing. 11 Evidence for heterogeneity of effects and transformed effects. 12 Combining evidence: fixed standardized effects model. 13 Combining evidence: random standardized effects mode. 14 Meta-regression. 15 Accounting for publication bias. Part II The Theory. 16 Calibrating evidence in a test. 17 The basics of variance stabilizing transformations. 18 One-sample binomial tests. 19 Two-sample binomial tests. 20 Defining evidence in t-statistics. 21 Two-sample comparisons. 22 Evidence in the chi-squared statistic. 23 Evidence in F-tests. 24 Evidence in Cochran's Q for heterogeneity of effects. 25 Combining evidence from K studies. 26 Correcting for publication bias. 27 Large-sample properties of variance stabilizing transformations. References. Index.