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Introduction to Meta-Analysis

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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
  • Clarifies concepts using text and figures, followed by formulas and examples
  • Explains how to avoid common mistakes in meta-analysis
  • Discusses controversies in meta-analysis
  • Features a web site with additional material and exercises

A superb combination of lucid prose and informative graphics, written by four of the world’s leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and Rothstein provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students, who used pre-publication versions of some of the chapters, raved about the clarity of the explanations and examples. David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics .

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. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. 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, ScDВ 

Introduction to Meta-Analysis is an excellent resource for novices and experts alike. The book provides a clear and comprehensive presentation of all basic and most advanced approaches to meta-analysis. This book will be referenced for decades. Michael A. McDaniel, Professor of Human Resources and Organizational Behavior, Virginia Commonwealth University

Author(s): Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein
Series: Statistics in Practice
Edition: 1
Publisher: Wiley
Year: 2009

Language: English
Commentary: 34304
Pages: 413

Cover......Page 1
Front......Page 2
Web Site......Page 19
WHAT THIS BOOK DOES NOT COVER......Page 20
Other meta-analytic methods......Page 26
00002.pdf......Page 29
Heterogeneity of effect sizes......Page 30
phairsp-hairspvalues......Page 32
phairsp-hairspvalue......Page 33
Consistency of effects......Page 35
00004.pdf......Page 41
Outline of effect size computations......Page 42
How to choose an effect size......Page 43
Response ratios......Page 45
Computing Dhairsp from studies that use independent groups......Page 46
Computing D from studies that use matched groups or pre-post scores......Page 47
Including different study designs in the same analysis......Page 49
Computing d and g from studies that use independent groups......Page 50
Computing d and g from studies that use pre-post scores or matched groups......Page 52
Including different study designs in the same analysis......Page 54
Choosing an effect size index......Page 57
Other approaches......Page 64
Converting from d to r......Page 67
STUDY DESIGN......Page 72
Concluding remarks......Page 76
Concluding Remarks......Page 77
00011.pdf......Page 78
Nomenclature......Page 79
Worked examples......Page 80
PERFORMING A FIXED-EFFECT META-ANALYSIS......Page 81
Illustrative example......Page 84
Performing a random-effects meta-analysis......Page 86
Estimating tau-squared......Page 89
Estimating the mean effect size......Page 90
Illustrative example......Page 91
CONCLUDING REMARKS......Page 93
Random effects......Page 99
A caveat......Page 100
WORKED EXAMPLE FOR CORRELATIONAL DATA (PART 1)......Page 103
00016.pdf......Page 119
Worked examples......Page 120
Confidence intervals (or uncertainty intervals) for Ihairsp2......Page 122
Comparing the confidence interval with the prediction interval......Page 141
Worked example for correlational data (Part 2)......Page 148
Confidence intervals for I 2......Page 151
Confidence intervals for I 2......Page 155
Confidence intervals for I 2......Page 159
Obtaining an overall effect in the presence of subgroups......Page 161
RANDOM-EFFECTS MODEL......Page 199
STATISTICAL POWER FOR SUBGROUP ANALYSES AND META-REGRESSION......Page 216
The null hypothesis under the different models......Page 217
Some technical considerations in random-effects meta-regression......Page 218
00023.pdf......Page 224
Overview......Page 225
Comparing subgroups......Page 227
Comparing Outcomes or Time-Points Within a Study......Page 234
Outline placeholder......Page 0
Computing a combined effect across outcomes......Page 236
Working with more than two outcomes per study......Page 239
When the correlation is unknown......Page 240
Computing a difference between outcomes......Page 243
Working with more than two outcomes per study......Page 244
When the correlation is unknown......Page 246
DIFFERENCES BETWEEN TREATMENTS......Page 248
Differences in effect......Page 252
00028.pdf......Page 255
Overview......Page 256
VOTE COUNTING IS A PERVASIVE PROBLEM......Page 257
Moving beyond the null......Page 260
Power analysis for a test of homogeneity......Page 262
Concluding remarks......Page 282
00032.pdf......Page 298
Overview......Page 299
NARRATIVE REVIEWS VS. META-ANALYSES......Page 300
AN EXAMPLE OF THE PARADOX......Page 306
BAYESIAN APPROACHES......Page 313
00036.pdf......Page 322
Overview......Page 323
Combining phairsp-VALUES......Page 324
One-Step (Peto) formula for odds ratio......Page 330
Concluding remarks......Page 339
Psychometric meta-analysis......Page 340
Explained variance in psychometric meta-analyses......Page 346
When covariates are not hypothesized in advance of the meta-analysis......Page 347
When there is an a priori hypothesis that a covariate may explain heterogeneity......Page 348
00040.pdf......Page 351
OVERVIEW......Page 352
How many studies are enough to carry out a meta-analysis?......Page 353
Randomized trials versus observational studies......Page 355
Can I combine studies that report results in different ways?......Page 357
Sensitivity analysis......Page 361
Why perform a cumulative meta-analysis?......Page 367
Cumulative meta-analysis as an educational tool......Page 369
To identify patterns in the data......Page 370
Using a cumulative analysis prospectively......Page 371
Concluding remarks......Page 373
Criticism......Page 374
Response......Page 375
Response......Page 376
Criticism......Page 377
Response......Page 378
Response......Page 380
00045.pdf......Page 384
Stata Macros With Stata 10.0......Page 385
Books, Web Sites and Professional Organizations......Page 398
Comprehensive Meta-Analysis......Page 399
The Human Genome Epidemiology Network......Page 400
References......Page 401
Index......Page 407