Introduction to Meta-Analysis

ISBN-10: 0470057246

ISBN-13: 9780470057247

Edition: 2008

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Description: Born from the teachings of a popular meta-analysis course, this text provides a concise and clearly presented discussion of all the elements in a meta-analysis. Starting with the explanation of basic concepts and will present a concise, clear discussion of all elements in meta-analysis. Many points are explained visually by using screenshots from Excel spreadsheets and computer programs such as Comprehensive Meta-Analysis (CMA) or Stata. Readers will also be encouraged to work through examples on their own using these programs, instructional versions of which will be provided by the book's website. Consequently this introductory text, written in an exceptionally clear style, would be ideal both for courses on meta-analysis, and for readers wanting to learn about it on their own.

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Book details

Copyright year: 2008
Publisher: John Wiley & Sons, Limited
Publication date: 3/13/2009
Binding: Hardcover
Pages: 452
Size: 7.00" wide x 9.75" long x 1.25" tall
Weight: 2.332
Language: English

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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