Introduction to Meta-Analysis

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

Autorentext
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.

Inhalt

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 of heterogeneity 111 Confidence intervals for tau² 114 Confidence intervals (or uncertainty intervals) for I² 115 Summary points 116 17 Prediction Intervals 119 Introduction 119 Prediction intervals in primary studies 119 Prediction intervals in meta-analysis 121 Confidence intervals and prediction intervals 123 Comparing the confidence interval with the prediction interval 123 Summary points 125 18 Worked Examples (Part 2) 127 Introduction 127 Worked example for continuous data (Part 2) 127 Worked example for binary data (Part 2) 131 Worked example for correlational data (Part 2) 134 Summary points 138 19 An Intuitive Look At Heterogeneity 139 Introduction 139 Motivating example 140 The Q-value and the p-value do not tell us howmuch the effect size varies 141 The confidence interval does not tell us how much the effect size varies 142 The I² statistic does not tell us how much the effect size varies 142 What I² tells us 142 The I² index vs. the prediction interval 145 The prediction interval 145 Prediction interval is clear, concise, and relevant 147 Computing the prediction interval 147 How to use I² 149 How to explain heterogeneity 149 How much does the effect size vary across studies? 150 Caveats 150 Conclusion 150 Further reading 151 Summary points 151 The meaning of I² in Figure 19.2 151 20 Classifying Heterogeneity As Low, Moderate, Or High 155 Introduction 155 Interest should generally focus on an index of absolute heterogeneity 155 The classifications lead themselves to mistakes of interpretation 158 Classifications focus attention in the wrong direction 158 Summary points 158 Part 5: Explaining Heterogeneity 21 Subgroup Analyses 161 Introduction 161 Fixed-effect model within subgroups 163 Computational models 172 Random effect…