Introduction to Meta-Analysis

Name: Introduction to Meta-Analysis
Price: 16.67 USD
Availability: InStock
ISBN: 9780470057247

ISBN-10: 0470057246

ISBN-13: 9780470057247

Edition: 2008

Authors: Michael Borenstein, Larry V. Hedges, Julian P. T. Higgins, Hannah R. Rothstein

List price: $34.95

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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…

Book details

List price: $34.95
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: 2.090
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