Detail View

List of Figures  
List of Tables
 
Acknowledgements
 
Preface
 
PART 1: INTRODUCTION
 
1 HOW A META-ANALYSIS WORKS
 
Introduction
 
Individual studies
 
The summary effect
 
Heterogeneity of effect sizes
 
Summary points
 
2 WHY PERFORM A META-ANALYSIS
 
Introduction
 
The SKIV meta-analysis
 
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
 
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
 
Further reading
 
PART 3: FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
 
10 OVERVIEW
 
Introduction
 
Nomenclature
 
11 FIXED-EFFECT MODEL
 
Introduction
 
The true effect size
 
Impact of sampling error
 
Performing a fixed-effect meta-analysis
 
Summary points
 
12 RANDOM-EFFECTS MODEL
 
Introduction
 
The true effect sizes
 
Impact of sampling error
 
Performing a random-effects meta-analysis
 
Summary points
 
13 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
 
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
 
16 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
 
17 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
 
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
 
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
 
20 META-REGRESSION
 
Introduction
 
Fixed-effect model
 
Fixed or random effects for unexplained heterogeneity
 
Random-effects model
 
Statistical power for regression
 
Summary points
 
21 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
 
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 TIME POINTS WITHIN A STUDY
 
Introduction
 
Combining across outcomes or time-points
 
Comparing outcomes or time-points 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
 
Combined 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 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
 
30 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
 
PART 7: ISSUES RELATED TO EFFECT SIZE
 
31 OVERVIEW
 
32 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
 
33 SIMPSON’S PARADOX
 
Introduction
 
Circumcision and risk of HIV infection
 
An example of the paradox
 
Summary points
 
34 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
 
PART 8: FURTHER METHODS
 
35 OVERVIEW
 
36 META-ANALYSIS METHODS BASED ON DIRECTION AND P -VALUES
 
Introduction
 
Vote counting
 
The sign test
 
Combining p-values
 
Summary points
 
37 FURTHER METHODS FOR DICHOTOMOUS DATA
 
Introduction
 
Mantel-Haenszel method
 
One-step (Peto) formula for odds ratio
 
Summary points
 
38 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
 
PART 9: META-ANALYSIS IN CONTEXT
 
39 OVERVIEW
 
40 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
 
41 REPORTING THE RESULTS OF A META-ANALYSIS
 
Introduction
 
The computational model
 
Forest plots
 
Sensitivity analysis
 
Summary points
 
42 CUMULATIVE META-ANALYSIS
 
Introduction
 
Why perform a cumulative meta-analysis?
 
Summary points
 
43 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
 
PART 10: RESOURCES AND SOFTWARE
 
44 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
 
45 BOOKS, WEB SITES AND PROFESSIONAL ORGANIZATIONS
 
Books on systematic review methods
 
Books on meta-analysis
 
Web sites
 
INDEX

Information Provided By: :