Michael Borenstein (USA Biostat Inc), Larry V. Hedges (US Northwestern University), Julian P. T. Higgins (UK Medical Research Council)

Introduction to Meta-Analysis

• Gebundenes Buch

Produktbeschreibung

This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. The first edition of this text was widely acclaimed for the clarity of the presentation, and quickly established itself as the definitive text in this field. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, and more. Several brand-new chapters provide a systematic "how to" approach to performing and reporting a meta-analysis from start to finish.

Written by four of the world's foremost authorities on all aspects of meta-analysis, the new edition:
_ Outlines the role of meta-analysis in the research process
_ Shows how to compute effects sizes and treatment effects
_ Explains the fixed-effect and random-effects models for synthesizing data
_ Demonstrates how to assess and interpret variation in effect size across studies
_ Explains how to avoid common mistakes in meta-analysis
_ Discusses controversies in meta-analysis
_ Includes access to a companion website containing videos, spreadsheets, data files, free software for prediction intervals, and step-by-step instructions for performing analyses using Comprehensive Meta-Analysis (CMA)(TM)

Download videos, class materials, and worked examples at www.Introduction-to-Meta-Analysis.com

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Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W119558350
• 2. Aufl.
• Seitenzahl: 544
• Erscheinungstermin: 13. Mai 2021
• Englisch
• Abmessung: 261mm x 182mm x 36mm
• Gewicht: 1176g
• ISBN-13: 9781119558354
• ISBN-10: 1119558352
• Artikelnr.: 60808784

Autorenporträt

Michael Borenstein is the Director of Biostat, a leading developer of statistical software. He is the primary developer of Comprehensive Meta-Analysis (CMA), the world's most widely used program for meta-analysis. He is the recipient of numerous grants from the NIH to develop methods, software, and educational materials for meta-analysis. He has lectured widely on meta-analysis, including at the NIH, CDC, and FDA. Larry V. Hedges is Board of Trustees Professor of Statistics and Education and Social Policy, Professor of Psychology, Professor of Medical Social Sciences, and IPR Fellow, Northwestern University, USA. He is a national leader in the fields of educational statistics and evaluation and is an elected member of many leading associations. Julian P.T. Higgins is Professor of Evidence Synthesis at the University of Bristol, UK, and a National Institute for Health Research (NIHR) Senior Investigator. He has had numerous core roles in the Cochrane Collaboration, including editing its methodological Handbook since 2003. His many contributions to meta-analysis include the foundation of network meta-analysis, methods for describing and explaining heterogeneity and a general framework for individual participant data meta-analysis. He is a Highly Cited Researcher with over a quarter of a million citations to his work and has been a recipient of the Ingram Olkin Award for distinguished lifetime achievement in research synthesis methodology. Hannah R. Rothstein is Professor of Management at Baruch College and the Graduate Center of the City University of New York. She is a Fellow of the American Psychological Association and a past President of the Society for Research Synthesis Methodology. She is former Editor-in-Chief of Research Synthesis Methods and serves on the editorial boards of Psychological Bulletin, Psychological Methods, and Organizational Research Methods. Professor Rothstein is a co-developer of the Comprehensive Meta-Analysis software and has published numerous systematic reviews and meta-analyses.

Inhaltsangabe

List of Tables xv

List of Figures xix

Acknowledgements xxv

Preface xxvii

Preface to the Second Edition xxxv

Website xxxvii

Part 1: Introduction

1 How a Meta-Analysis Works 3

Introduction 3

Individual studies 3

The summary effect 5

Heterogeneity of effect sizes 6

Summary points 7

2 Why Perform a Meta-Analysis 9

Introduction 9

The streptokinase meta-analysis 10

Statistical significance 11

Clinical importance of the effect 11

Consistency of effects 12

Summary points 13

Part 2: Effect Size and Precision

3 Overview 17

Treatment effects and effect sizes 17

Parameters and estimates 18

Outline of effect size computations 19

4 Effect Sizes Based On Means 21

Introduction 21

Raw (unstandardized) mean difference D 21

Standardized mean difference, d and g 25

Response ratios 30

Summary points 31

5 Effect Sizes Based On Binary Data (2 × 2 Tables) 33

Introduction 33

Risk ratio 33

Odds ratio 35

Risk difference 37

Choosing an effect size index 38

Summary points 38

6 Effect Sizes Based On Correlations 39

Introduction 39

Computing r 39

Other approaches 40

Summary points 41

7 Converting Among Effect Sizes 43

Introduction 43

Converting from the log odds ratio to d 44

Converting from d to the log odds ratio 45

Converting from r to d 45

Converting from d to r 46

Summary points 47

8 Factors That Affect Precision 49

Introduction 49

Factors that affect precision 50

Sample size 50

Study design 51

Summary points 53

9 Concluding Remarks 55

Part 3: Fixed-Effect Versus Random-Effects Models

10 Overview 59

Introduction 59

Nomenclature 60

11 Fixed-Effect Model 61

Introduction 61

The true effect size 61

Impact of sampling error 61

Performing a fixed-effect meta-analysis 63

Summary points 64

12 Random-Effects Model 65

Introduction 65

The true effect sizes 65

Impact of sampling error 66

Performing a random-effects meta-analysis 68

Summary points 70

13 Fixed-Effect Versus Random-Effects Models 71

Introduction 71

Definition of a summary effect 71

Estimating the summary effect 72

Extreme effect size in a large study or a small study 73

Confidence interval 73

The null hypothesis 76

Which model should we use? 76

Model should not be based on the test for heterogeneity 78

Concluding remarks 79

Summary points 79

14 Worked Examples (Part 1) 81

Introduction 81

Worked example for continuous data (Part 1) 81

Worked example for binary data (Part 1) 85

Worked example for correlational data (Part 1) 90

Summary points 94

Part 4: Heterogeneity

15 Overview 97

Introduction 97

Nomenclature 98

Worked examples 98

16 Identifying and Quantifying Heterogeneity 99

Introduction 99

Isolating the variation in true effects 99

Computing Q 101

Estimating tau² 106

The I² statistic 109

Comparing the measures