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Click to enlarge See the back cover 
Introduction to MetaAnalysis 

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Comment from the Stata technical groupMetaanalysis has gained increasing popularity since the early 1990s as a way to synthesize the results from separate studies. It is widely used in the medical sciences, education, and business. This text is both complete and current, and is ideal for researchers wanting a conceptual treatment of the methodology. A chapter on statistical software for performing metaanalysis (including how to do so in Stata) is also included. 

Table of contentsView table of contents >> List of Tables
List of Figures
Acknowledgments
Preface
Website
Part 1: Introduction
1 How a metaanalysis works
Introduction
Individual studies The summary effect Heterogeneity of effect sizes Summary points 2 Why perform a metaanalysis
Introduction
The streptokinase metaanalysis Statistical significance Clinical importance of the effect Consistency of effects Summary points Part 2: Effect size and precision
3 Overview
Treatment effects and effect sizes
Parameters and estimates Outline of effect size computations 4 Effect sizes based on means
Introduction
Raw (unstandardized) mean difference D Standardized mean difference, d and g Response ratios Summary points 5 Effect sizes based on binary data (2 × 2 tables)
Introduction
Risk ratio Odds ratio Risk difference Choosing an effect size index Summary points 6 Effect sizes based on correlations
Introduction
Computing r Other approaches Summary points 7 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 8 Factors that affect precision
Introduction
Factors that affect precision Sample size Study design Summary points 9 Concluding remarks
Part 3: Fixedeffect versus randomeffects models
10 Overview
Introduction
Nomenclature 11 Fixedeffect model
Introduction
The true effect size Impact of sampling error Performing a fixedeffect metaanalysis Summary points 12 Randomeffects model
Introduction
The true effect sizes Impact of sampling error Performing a randomeffects metaanalysis Summary points 13 Fixedeffect versus randomeffects models
Introduction
Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small 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 14 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 Part 4: Heterogeneity
15 Overview
Introduction
Nomenclature Worked examples 16 Identifying and quantifying heterogeneity
Introduction
Isolating the variation in true effects Computing Q Estimating τ^{2} The I^{2} statistic Comparing the measures of heterogeneity Confidence intervals for τ^{2} Confidence intervals (or uncertainty intervals) for I^{2} Summary points 17 Prediction intervals
Introduction
Prediction intervals in primary studies Prediction intervals in metaanalysis Confidence intervals and prediction intervals Comparing the confidence interval with the prediction interval Summary points 18 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 19 Subgroup analyses
Introduction
Fixedeffect model within subgroups Computational models Random effects with separate estimates of τ^{2} Random effects with pooled estimate of τ^{2} The proportion of variance explained Mixedeffects model Obtaining an overall effect in the presence of subgroups Summary points 20 Metaregression
Introduction
Fixedeffect model Fixed or random effects for unexplained heterogeneity Randomeffects model Summary points 21 Notes on subgroup analyses and metaregression
Introduction
Computational model Multiple comparisons Software Analyses of subgroups and regression analyses are observational Statistical power for subgroup analyses and metaregression Summary points Part 5: Complex data structures
22 Overview
23 Independent subgroups within a study
Introduction
Combining across subgroups Comparing subgroups Summary points 24 Multiple outcomes or timepoints within a study
Introduction
Combining across outcomes or timepoints Comparing outcomes or timepoints within a study Summary points 25 Multiple comparisons within a study
Introduction
Combining across multiple comparisons within a study Differences between treatments Summary points 26 Notes on complex data structures
Introduction
Summary effect Differences in effect Part 6: Other issues
27 Overview
28 Vote counting—A new name for an old problem
Introduction
Why vote counting is wrong Vote counting is a pervasive problem Summary points 29 Power analysis for metaanalysis
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 metaanalysis Power analysis for a test of homogeneity Summary points 30 Publication bias
Introduction
The problem of missing studies Methods for addressing bias Illustrative example The model Getting a sense of the data Is there evidence of any bias? 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 Some important caveats Smallstudy effects Concluding remarks Summary points Part 7: Issues related to effect size
31 Overview
32 Effect sizes rather than pvalues
Introduction
Relationship between pvalues and effect sizes The distinction is important The pvalue is often misinterpreted Narrative reviews vs. metaanalyses Summary points 33 Simpson’s paradox
Introduction
Circumcision and risk of HIV infection An example of the paradox Summary points 34 Generality of the basic inversevariance method
Introduction
Other effect sizes Other methods for estimating effect sizes Individual participant data metaanalyses Bayesian approaches Summary points Part 8: Further methods
35 Overview
36 Metaanalysis methods based on direction and pvalues
Introduction
Vote counting The sign test Combining pvalues Summary points 37 Further methods for dichotomous data
Introduction
Mantel–Haenszel method Onestep (Peto) formula for odds ratio Summary points 38 Psychometric metaanalysis
Introduction
The attenuating effects of artifacts Metaanalysis methods Example of psychometric metaanalysis Comparison of artifact correction with metaregression Sources of information about artifact values How heterogeneity is assessed Reporting in psychometric metaanalysis Concluding remarks Summary points Part 9: Metaanalysis in context
39 Overview
40 When does it make sense to perform a metaanalysis?
Introduction
Are the studies similar enough to combine? Can I combine studies with different designs? How many studies are enough to carry out a metaanalysis? Summary points 41 Reporting the results of a metaanalysis
Introduction
The computational model Forest plots Sensitivity analysis Summary points 42 Cumulative metaanalysis
Introduction
Why perform a cumulative metaanalysis? Summary points 43 Criticisms of metaanalysis
Introduction
One number cannot summarize a research field The file drawer problem invalidates metaanalysis Mixing apples and oranges Garbage in, garbage out Important studies are ignored Metaanalysis can disagree with randomized trials Metaanalyses are performed poorly Is a narrative review better? Concluding remarks Summary points Part 10: Resources and software
44 Software
Introduction
The software Three examples of metaanalysis software Comprehensive MetaAnalysis (CMA) 2.0 RevMan 5.0 Stata macros with Stata 10.0 Summary points 45 Books, websites and professional organizations
Books on systematic review methods
Books on metaanalysis Websites References
Index

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