Table of Contents
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List of Tables |
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List of Figures |
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Acknowledgements |
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Preface |
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Web Site |
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PART
1 |
INTRODUCTION |
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1 |
How a Meta-Analysis Works |
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Introduction |
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Individual
Studies |
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The Summary
Effect |
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Heterogeneity
of Effect Sizes |
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Summary Points |
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2 |
Why Perform a Meta-Analysis |
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Introduction |
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The
Streptokinase Meta-Analysis |
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Statistical
Significance |
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Clinical
Importance of the Effect |
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Consistency of
Effects |
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Summary Points |
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PART
2 |
EFFECT
SIZE AND PRECISION |
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3 |
Overview |
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Treatment
Effects and Effect Sizes |
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Parameters and
Estimates |
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Outline of
Effect Size Computations |
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4 |
Effect Sizes Based on Means |
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Introduction |
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Raw
(Unstandardized) Mean Difference D |
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Standardized
Mean Difference, d and g |
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Response Ratios |
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Summary Points |
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5 |
Effect Sizes Based on Binary
Data (2 × 2 Tables) |
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Introduction |
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Risk Ratio |
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Odds Ratio |
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Risk Difference |
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Choosing an
Effect Size Index |
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Summary Points |
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6 |
Effect Sizes Based on
Correlations |
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Introduction |
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Computing r |
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Other Approaches |
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Summary Points |
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7 |
Converting Among Effect Sizes |
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Introduction |
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Converting from
the Log Odds Ratio to d |
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Converting from
d to the Log Odds Ratio |
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Converting from
r to d |
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Converting from
d to r |
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Summary Points |
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8 |
Factors that Affect Precision |
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Introduction |
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Factors that
Affect Precision |
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Sample Size |
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Study Design |
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Summary Points |
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9 |
Concluding Remarks |
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PART
3 |
FIXED-EFFECT
VERSUS RANDOM-EFFECTS MODELS |
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10 |
Overview |
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Introduction |
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Nomenclature |
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11 |
Fixed-Effect Model |
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Introduction |
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The True Effect
Size |
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Impact of
Sampling Error |
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Performing a
Fixed-Effect Meta-Analysis |
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Summary Points |
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12 |
Random-Effects Model |
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Introduction |
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The True Effect
Sizes |
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Impact of
Sampling Error |
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Performing a
Random-Effects Meta-Analysis |
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Summary Points |
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13 |
Fixed-Effect versus
Random-Effects Models |
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Introduction |
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Definition of a
Summary Effect |
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Estimating the
Summary Effect |
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Extreme Effect
Size in a Large Study or a Small Study |
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Confidence
Interval |
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The Null
Hypothesis |
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Which Model
should We Use? |
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Model should
Not be Based on the Test for Heterogeneity |
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Concluding
Remarks |
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Summary Points |
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14 |
Worked Examples (Part 1) |
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Introduction |
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Worked Example
for Continuous Data (Part 1) |
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Worked Example
for Binary Data (Part 1) |
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Worked Example
for Correlational Data (Part 1) |
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Summary Points |
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PART
4 |
HETEROGENEITY |
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15 |
Overview |
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Introduction |
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Nomenclature |
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Worked Examples |
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16 |
Identifying and Quantifying
Heterogeneity |
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Introduction |
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Isolating the
Variation in True Effects |
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Computing Q |
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Estimating r2 |
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The I2 Statistic |
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Comparing the
Measures of Heterogeneity |
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Confidence
Intervals for r2 |
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Confidence
Intervals (or Uncertainty Intervals) for I2 |
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Summary Points |
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17 |
Prediction Intervals |
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Introduction |
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Prediction
Intervals in Primary Studies |
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Prediction
Intervals in Meta-Analysis |
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Confidence
Intervals and Prediction Intervals |
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Comparing the
Confidence Interval with the Prediction Interval |
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Summary Points |
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18 |
Worked Examples (Part 2) |
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Introduction |
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Worked Example
for Continuous Data (Part 2) |
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Worked Example
for Binary Data (Part 2) |
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Worked Example
for Correlational Data (Part 2) |
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Summary Points |
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19 |
Subgroup Analyses |
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Introduction |
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Fixed-Effect
Model within Subgroups |
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Computational
Models |
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Random Effects
with Separate Estimates of r2 |
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Random Effects
with Pooled Estimate of r2 |
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The Proportion
of Variance Explained |
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Mixed-Effects
Model |
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Obtaining an
Overall Effect in the Presence of Subgroups |
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Summary Points |
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20 |
Meta-Regression |
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Introduction |
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Fixed-Effect
Model |
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Fixed or Random
Effects for Unexplained Heterogeneity |
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Random-Effects
Model |
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Summary Points |
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21 |
Notes on Subgroup Analyses and
Meta-Regression |
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Introduction |
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Computational
Model |
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Multiple
Comparisons |
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Software |
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Analyses of
Subgroups and Regression Analyses are Observational |
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Statistical
Power for Subgroup Analyses and Meta-Regression |
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Summary Points |
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PART
5 |
COMPLEX
DATA STRUCTURES |
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22 |
Overview |
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23 |
Independent Subgroups within a
Study |
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Introduction |
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Combining
Across Subgroups |
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Comparing
Subgroups |
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Summary Points |
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24 |
Multiple Outcomes or Time-Points
within a Study |
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Introduction |
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Combining
Across Outcomes or Time-Points |
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Comparing
Outcomes or Time-Points within a Study |
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Summary Points |
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25 |
Multiple Comparisons within a
Study |
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Introduction |
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Combining
Across Multiple Comparisons within a Study |
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Differences
Between Treatments |
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Summary Points |
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26 |
Notes on Complex Data Structures |
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Introduction |
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Summary Effect |
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Differences in
Effect |
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PART
6 |
OTHER
ISSUES |
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27 |
Overview |
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28 |
Vote Counting – A New Name for
an Old Problem |
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Introduction |
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Why Vote
Counting is Wrong |
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Vote Counting
is a Pervasive Problem |
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Summary Points |
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29 |
Power Analysis for Meta-Analysis |
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Introduction |
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A Conceptual
Approach |
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In Context |
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When to Use
Power Analysis |
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Planning for
Precision Rather Than for Power |
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Power Analysis
in Primary Studies |
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Power Analysis
for Meta-Analysis |
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Power Analysis
for a Test of Homogeneity |
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Summary Points |
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30 |
Publication Bias |
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Introduction |
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The Problem of
Missing Studies |
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Methods for
Addressing Bias |
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Illustrative
Example |
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The Model |
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Getting a Sense
of the Data |
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Is there
Evidence of Any Bias? |
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Is the Entire
Effect an Artifact of Bias? |
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How Much of an
Impact might the Bias Have? |
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Summary of the
Findings for the Illustrative Example |
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Some Important
Caveats |
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Small-Study
Effects |
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Concluding
Remarks |
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Summary Points |
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PART
7 |
ISSUES
RELATED TO EFFECT SIZE |
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31 |
Overview |
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32 |
Effect Sizes Rather Than P-Values |
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Introduction |
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Relationship
Between P-Values and Effect Sizes |
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The Distinction
is Important |
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The P-Value is Often Misinterpreted |
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Narrative
Reviews vs. Meta-Analyses |
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Summary Points |
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33 |
Simpson’s Paradox |
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Introduction |
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Circumcision
and Risk of HIV Infection |
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An Example of
the Paradox |
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Summary Points |
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34 |
Generality of the Basic
Inverse-Variance Method |
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Introduction |
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Other Effect
Sizes |
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Other Methods
for Estimating Effect Sizes |
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Individual
Participant Data Meta-Analyses |
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Bayesian
Approaches |
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Summary Points |
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PART
8 |
FURTHER
METHODS |
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35 |
Overview |
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36 |
Meta-Analysis Methods Based on
Direction and P –Values |
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Introduction |
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Vote Counting |
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The Sign Test
Combining P-Values |
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Summary Points |
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37 |
Further Methods for Dichotomous
Data |
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Introduction |
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Mantel-Haenszel
Method |
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One-Step (Peto)
Formula for Odds Ratio |
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Summary Points |
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38 |
Psychometric Meta-Analysis |
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Introduction |
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The Attenuating
Effects of Artifacts Meta-Analysis Methods |
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Example of
Psychometric Meta-Analysis |
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Comparison of
Artifact Correction with Meta-Regression |
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Sources of
Information About Artifact Values |
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How
Heterogeneity is Assessed |
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Reporting in
Psychometric Meta-Analysis |
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Concluding
Remarks |
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Summary Points |
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PART
9 |
META-ANALYSIS
IN CONTEXT |
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39 |
Overview |
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40 |
When Does it Make Sense to
Perform a Meta-Analysis? |
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Introduction |
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Are the Studies
Similar Enough to Combine? |
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Can I Combine
Studies with Different Designs? |
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How Many
Studies are Enough to Carry Out a Meta-Analysis? |
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Summary Points |
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41 |
Reporting the Results of a
Meta-Analysis |
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Introduction |
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The
Computational Model |
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Sensitivity
Analysis |
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Summary Points |
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42 |
Cumulative Meta-Analysis |
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Introduction |
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Why Perform a
Cumulative Meta-Analysis? |
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Summary Points |
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43 |
Criticisms of Meta-Analysis |
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Introduction |
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One Number
Cannot Summarize a Research Field |
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The File Drawer
Problem Invalidates Meta-Analysis |
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Mixing Apples
and |
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Garbage In,
Garbage Out |
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Important
Studies are Ignored |
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Meta-Analysis
can Disagree with Randomized Trials |
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Meta-Analyses
are Performed Poorly |
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Is a Narrative
Review Better? |
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Concluding
Remarks |
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Summary Points |
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PART
10 |
RESOURCES
AND SOFTWARE |
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44 |
Software |
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Introduction |
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The Software |
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Three Examples
of Meta-Analysis Software |
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Comprehensive
Meta-Analysis (CMA) 2.0 |
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RevMan 5.0 |
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Stata Macros
with Stata 10.0 |
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Summary Points |
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45 |
Books, Web Sites and
Professional Organizations |
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Books on
Systematic Review Methods |
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Books on
Meta-Analysis |
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Web Sites |
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References |
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Index |
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