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. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology.Introduction to meta-analysis: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 sections on additional readings and other resources.The book's approach is primarily conceptual, but with sufficient detail so that readers can implement the procedures in their own research. Key ideas are introduced using text and figures, followed by the relevant formulas and worked examples. These examples, as well as additional exercises and materials, may be downloaded from the book's web site (www.Meta-Analysis.com).The authors have extensive experience in the theory and application of meta-analysis. As a group, they have hundreds of publications in this area, as well as many years of experience teaching meta-analysis to researchers, clinicians and statisticians. This book builds on this foundation and will serve equally well as the text for a course in meta-analysis, as a resource for self-study, or as a reference.The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas, and worked examples provide a superb practical guide to performing a meta-analysis. Unlike other references in meta-analysis, which tend to focus on specific fields of research, Introduction to Meta-analysis provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. The summary points at the end of each chapter and the notes at the end are extremely useful. The sections on Simpson's paradox, and a unique perspective on meta-analysis in context are nothing short of gems. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice. Jesse A. Berlin, ScD

Michael Borenstein, Director of Biostatistical Programming Associates
Professor Borenstein is the co-editor of the recently published Wiley book Publication Bias in Meta-Analysis, and has taught dozens of workshops on meta-analysis. He also helped to develop the best-selling software programs for statistical power analysis.

Hannah Rothstein, Zicklin School of Business, Baruch College
Professor Rothstein teaches regular seminars on meta-analysis and systematic reviews, and has 20 years of active research in the area of meta-analysis. She has authored several meta-analyses as well as articles on methodological issues in the area, and made numerous presentations on the topic. Having contributed chapters to two books on meta-analysis, she co-edited Publication Bias in Meta-Analysis.

Larry Hedges, University of Chicago
A pioneer in meta-analysis, Professor Hedges has published over 80 papers in the area (many describing techniques he himself developed, that are now used as standard), co-edited the Handbook for Synthesis Research, and co-authored three books on the topic including the seminal Statistical Methods for Meta-Analysis. He has also taught numerous short courses on meta-analysis sponsored by various international organizations such as the ASA.

Julian Higgins, MRC Biostatistics Unit, Cambridge
Dr Higgins has published many methodological papers in meta-analysis. He works closely with the Cochrane Collaboration and is an editor of the Cochrane Handbook. He has much experience of teaching meta-analysis, both at Cambridge University and, by invitation, around the world.

List of Figures
List of Tables
Acknowledgements
Preface
Introduction
How a meta-analysis works
Introduction
Individual studies
The summary effect
Heterogeneity of effect sizes
Summary points
Why Perform a Meta-Analysis
Introduction
The SKIV meta-analysis
Statistical significance
Clinical importance of the effect
Consistency of effects
Summary points
Effect Size and Precision
Overview
Treatment effects and effect sizes
Parameters and estimates
Outline
Effect Sizes Based on Means
Introduction
Raw (unstandardized) mean difference D
Standardized mean difference, D and G
Response ratiosSummary points
Effect Sizes Based on Binary Data (2+2 Tables)
Introduction
Risk ratio
Odds ratio
Risk difference
Choosing an effect size index
Summary points
Effect Sizes Based on Correlations
Introduction
Computing R
Other approaches
Summary points
Converting Among Effect Sizes
Introduction
Converting from the log odds ratio to D
Converting from D to the log odds ratio
Converting from R to D
Converting from D to R
Summary points
Factors that Affect Precision
Introduction
Factors that affect precision
Sample size
Study design
Summary points
Concluding Remarks
Further reading
Fixed-Effect Versus Random-Effects Models
Overview
Introduction
Nomenclature
Fixed-Effect Model
Introduction
The true effect size
Impact of sampling error
Performing a fixed-effect meta-analysis
Summary points
Random-effects model
Introduction
The true effect sizes
Impact of sampling error
Performing a random-effects meta-analysis
Summary points
Fixed Effect Versus Random-Effects Models
Introduction
Definition of a summary effect
Estimating the summary effect
Extreme effect size in large study
Confidence interval
The null hypothesis
Which model should we use?
Model should not be based on the test for heterogeneity
Concluding remarks
Summary points
Worked Examples (Part 1)
Introduction
Worked example for continuous data (Part 1)
Worked example for binary data (Part 1)
Worked example for correlational data (Part 1)
Summary points
Heterogeneity
Overview
Introduction
Identifying and Quantifying Heterogeneity
Introduction
Isolating the variation in true effects
Computing Q
Estimating tau-squared
The I 2 statistic
Comparing the measures of heterogeneity
Confidence intervals for T 2
Confidence intervals (or uncertainty intervals) for I 2
Summary points
Prediction Intervals
Introduction
Prediction intervals in primary studies
Prediction intervals in meta-analysis
Confidence intervals and prediction intervals
Comparing the confidence interval with the prediction interval
Summary points
Worked Examples (Part 2)
Introduction
Worked example for continuous data (Part 2)
Worked example for binary data (Part 2)
Worked example for correlational data (Part 2)
Summary points
Subgroup Analyse
Introduction
Fixed-effect model within subgroups
Computational models
Random effects with separate estimates of T 2
Random effects with pooled estimate of T 2
The proportion of variance explained
Mixed-effect model
Obtaining an overall effect in the presence of subgroups
Summary points
Meta-Regress
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Introduction to Meta-Analysis

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Author Biography

Table of Contents

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