Introduction to Meta-Analysis

by Michael Borenstein (Biostat Inc, New Jersey); Larry V. Hedges (Northwestern University, US); Julian P. T. Higgins (Medical Research Council, UK); Hannah R. Rothstein (Baruch College, New York, New York)

• ISBN13: 9780470743379
• ISBN10: 0470743379
• Format: eBook
• Copyright: 2009-03-01
• 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

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 ratios

Summary 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 Analyses

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-Regression

Introduction

Fixed-effect model

Fixed or random effects for unexplained heterogeneity

Random-effects model

Statistical power for regression

Summary points

Notes On Subgroup Analyses And Meta-Regression

Introduction

Computational model

Multiple comparisons

Software

Analysis of subgroups and regression are observational

Statistical power for subgroup analyses and meta-regression

Summary points

Complex Data Structures

Overview

Independent Subgroups Within A Study

Introduction

Combining across subgroups

Comparing subgroups

Summary points

Multiple Outcomes Or Time Points Within A Study

Introduction

Combining across outcomes or time-points

Comparing outcomes or time-points within a study

Summary points

Multiple Comparisons Within A Study

Introduction

Combining across multiple comparisons within a study

Differences between treatments

Summary points

Notes On Complex Data Structures

Introduction

Combined effect

Differences in effect

Other Issues

Overview

Vote Counting - A New Name For An Old Problem

Introduction

Why vote counting is wrong

Vote-counting is a pervasive problem

Summary points

Power Analysis For Meta-Analysis

Introduction

A conceptual approach

In context

When to use power analysis

Planning for precision rather than for power

Power analysis in primary studies

Power analysis for meta-analysis

Power analysis for a test of homogeneity

Summary points

Publication Bias

Introduction

The problem of missing studies

Methods for addressing bias

Illustrative example

The model

Getting a sense of the data

Is the entire effect an artifact of bias

How much of an impact might the bias have?

Summary of the findings for the illustrative example

Small study effects

Concluding remarks

Summary points

Issues Related To Effect Size

Overview

Effect Sizes Rather Than P -Values

Introduction

Relationship between p-values and effect sizes

The distinction is important

The p-value is often misinterpreted

Narrative reviews vs. meta-analyses

Summary points

Simpson's Paradox

Introduction

Circumcision and risk of HIV infection

An example of the paradox

Summary points

Generality Of The Basic Inverse-Variance Method

Introduction

Other effect sizes

Other methods for estimating effect sizes

Individual participant data meta-analyses

Bayesian approaches

Summary points

Further Methods

Overview

Meta-Analysis Methods Based On Direction And P -Values

Introduction

Vote counting

The sign test

Combining p-values

Summary points

Further Methods For Dichotomous Data

Introduction

Mantel-Haenszel method

One-step (Peto) formula for odds ratio

Summary points

Psychometric Meta-Analysis

Introduction

The attenuating effects of artifacts

Meta-analysis methods

Example of psychometric meta-analysis

Comparison of artifact correction with meta-regression

Sources of information about artifact values

How heterogeneity is assessed

Reporting in psychometric meta-analysis

Concluding remarks

Summary points

Meta-Analysis In Context

Overview

When Does It Make Sense To Perform A Meta-Analysis?

Introduction

Are the studies similar enough to combine?

Can I combine studies with different designs?

How many studies are enough to carry out a meta-analysis?

Summary points

Reporting The Results Of A Meta-Analysis

Introduction

The computational model

Forest plots

Sensitivity analysis

Summary points

Cumulative Meta-Analysis

Introduction

Why perform a cumulative meta-analysis?

Summary points

Criticisms Of Meta-Analysis

Introduction

One number cannot summarize a research field

The file drawer problem invalidates meta-analysis

Mixing apples and oranges

Garbage in, garbage out

Important studies are ignored

Meta-analysis can disagree with randomized trials

Meta-analyses are performed poorly

Is a narrative review better?

Concluding remarks

Summary points

Resources And Software

Software

Introduction

Three examples of meta-analysis software

The software

Comprehensive meta-analysis (CMA) 2.0

Revman 5.0

StataTM macros with Stata 10.0

Summary points

Books, Web Sites And Professional Organizations

Books on systematic review methods

Books on meta-analysis

Web sites

Index

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