Introduction to Meta-Analysis

by Borenstein, Michael; Hedges, Larry V.; Higgins, Julian P. T.; Rothstein, Hannah R.

• ISBN13: 9780470057247
• ISBN10: 0470057246
• Edition: 1st
• Format: Hardcover
• Copyright: 2009-04-13
• Publisher: Wiley

Summary

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

Author Biography

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.

Table of Contents

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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