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Introduction to Meta-Analysis 2nd edition [Kõva köide]

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(Baruch College, New York, USA), (Biostat Inc, USA), (Northwestern University, US), (Medical Research Council, UK)
  • Formaat: Hardback, 544 pages, kõrgus x laius x paksus: 254x178x35 mm, kaal: 1162 g
  • Ilmumisaeg: 13-May-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119558352
  • ISBN-13: 9781119558354
Teised raamatud teemal:
Introduction to Meta-Analysis 2nd editionSuurem pilt
  • Formaat: Hardback, 544 pages, kõrgus x laius x paksus: 254x178x35 mm, kaal: 1162 g
  • Ilmumisaeg: 13-May-2021
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119558352
  • ISBN-13: 9781119558354
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119558354.html
Märksõnad:
A clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies

The first edition of this text was widely acclaimed for the clarity of the presentation, and quickly established itself as the definitive text in this field. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, and more. Several brand-new chapters provide a systematic "how to" approach to performing and reporting a meta-analysis from start to finish.

Written by four of the world's foremost authorities on all aspects of meta-analysis, the new edition:





Outlines the role of meta-analysis in the research process Shows how to compute effects sizes and treatment effects Explains the fixed-effect and random-effects models for synthesizing data Demonstrates how to assess and interpret variation in effect size across studies Explains how to avoid common mistakes in meta-analysis Discusses controversies in meta-analysis Includes access to a companion website containing videos, spreadsheets, data files, free software for prediction intervals, and step-by-step instructions for performing analyses using Comprehensive Meta-Analysis (CMA)

Download videos, class materials, and worked examples at www.Introduction-to-Meta-Analysis.com

"This book offers the reader a unified framework for thinking about meta-analysis, and then discusses all elements of the analysis within that framework. The authors address a series of common mistakes and explain how to avoid them. As the editor-in-chief of the American Psychologist and former editor of Psychological Bulletin, I can say without hesitation that the quality of manuscript submissions reporting meta-analyses would be vastly better if researchers read this book." Harris Cooper, Hugo L. Blomquist Distinguished Professor Emeritus of Psychology and Neuroscience, Editor-in-chief of the American Psychologist, former editor of Psychological Bulletin

"A superb combination of lucid prose and informative graphics, the authors provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students raved about the clarity of the explanations and examples." David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics

"The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice." Jesse A. Berlin, SCD
List of Tables
xv
List of Figures
xix
Acknowledgements xxv
Preface xxvii
Preface to the Second Edition xxxv
Website xxxvii
PART 1 INTRODUCTION
1 How A Meta-Analysis Works
3(6)
Introduction
3(1)
Individual studies
3(2)
The summary effect
5(1)
Heterogeneity of effect sizes
6(1)
Summary points
7(2)
2 Why Perform A Meta-Analysis
9(8)
Introduction
9(1)
The streptokinase meta-analysis
10(1)
Statistical significance
11(1)
Clinical importance of the effect
11(1)
Consistency of effects
12(1)
Summary points
13(4)
PART 2 EFFECT SIZE AND PRECISION
3 Overview
17(4)
Treatment effects and effect sizes
17(1)
Parameters and estimates
18(1)
Outline of effect size computations
19(2)
4 Effect Sizes Based On Means
21(12)
Introduction
21(1)
Raw (unstandardized) mean difference D
21(4)
Standardized mean difference, d and g
25(5)
Response ratios
30(1)
Summary points
31(2)
5 Effect Sizes Based On Binary Data (2 × 2 Tables)
33(6)
Introduction
33(1)
Risk ratio
33(2)
Odds ratio
35(2)
Risk difference
37(1)
Choosing an effect size index
38(1)
Summary points
38(1)
6 Effect Sizes Based On Correlations
39(4)
Introduction
39(1)
Computing r
39(1)
Other approaches
40(1)
Summary points
41(2)
7 Converting Among Effect Sizes
43(6)
Introduction
43(1)
Converting from the log odds ratio to d
44(1)
Converting from d to the log odds ratio
45(1)
Converting from r to d
45(1)
Converting from d to r
46(1)
Summary points
47(2)
8 Factors That Affect Precision
49(6)
Introduction
49(1)
Factors that affect precision
50(1)
Sample size
50(1)
Study design
51(2)
Summary points
53(2)
9 Concluding Remarks
55(4)
PART 3 FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
10 Overview
59(2)
Introduction
59(1)
Nomenclature
60(1)
11 Fixed-Effect Model
61(4)
Introduction
61(1)
The true effect size
61(1)
Impact of sampling error
61(2)
Performing a fixed-effect meta-analysis
63(1)
Summary points
64(1)
12 Random-Effects Model
65(6)
Introduction
65(1)
The true effect sizes
65(1)
Impact of sampling error
66(2)
Performing a random-effects meta-analysis
68(2)
Summary points
70(1)
13 Fixed-Effect Versus Random-Effects Models
71(10)
Introduction
71(1)
Definition of a summary effect
71(1)
Estimating the summary effect
72(1)
Extreme effect size in a large study or a small study
73(1)
Confidence interval
73(3)
The null hypothesis
76(1)
Which model should we use?
76(2)
Model should not be based on the test for heterogeneity
78(1)
Concluding remarks
79(1)
Summary points
79(2)
14 Worked Examples (Part 1)
81(16)
Introduction
81(1)
Worked example for continuous data (Part 1)
81(4)
Worked example for binary data (Part 1)
85(5)
Worked example for correlational data (Part 1)
90(4)
Summary points
94(3)
PART 4 HETEROGENEITY
15 Overview
97(2)
Introduction
97(1)
Nomenclature
98(1)
Worked examples
98(1)
16 Identifying and Quantifying Heterogeneity
99(20)
Introduction
99(1)
Isolating the variation in true effects
99(2)
Computing Q
101(5)
Estimating τ2
106(3)
The I2 statistic
109(2)
Comparing the measures of heterogeneity
111(3)
Confidence intervals for τ2
114(1)
Confidence intervals (or uncertainty intervals) for I2
115(1)
Summary points
116(3)
17 Prediction Intervals
119(8)
Introduction
119(1)
Prediction intervals in primary studies
119(2)
Prediction intervals in meta-analysis
121(2)
Confidence intervals and prediction intervals
123(1)
Comparing the confidence interval with the prediction interval
123(2)
Summary points
125(2)
18 Worked Examples (Part 2)
127(12)
Introduction
127(1)
Worked example for continuous data (Part 2)
127(4)
Worked example for binary data (Part 2)
131(3)
Worked example for correlational data (Part 2)
134(4)
Summary points
138(1)
19 An Intuitive Look At Heterogeneity
139(16)
Introduction
139(1)
Motivating example
140(1)
The Q-value and the p-value do not tell us how much the effect size varies
141(1)
The confidence interval does not tell us how much the effect size varies
142(1)
The I2 statistic does not tell us how much the effect size varies
142(1)
What I2 tells us
142(3)
The I2 index vs. the prediction interval
145(1)
The prediction interval
145(2)
Prediction interval is clear, concise, and relevant
147(1)
Computing the prediction interval
147(2)
How to use I2
149(1)
How to explain heterogeneity
149(1)
How much does the effect size vary across studies?
150(1)
Caveats
150(1)
Conclusion
150(1)
Further reading
151(1)
Summary points
151(1)
The meaning of I2 in Figure 19.2
151(4)
20 Classifying Heterogeneity As Low, Moderate, Or High
155(6)
Introduction
155(1)
Interest should generally focus on an index of absolute heterogeneity
155(3)
The classifications lead themselves to mistakes of interpretation
158(1)
Classifications focus attention in the wrong direction
158(1)
Summary points
158(3)
PART 5 EXPLAINING HETEROGENEITY
21 Subgroup Analyses
161(36)
Introduction
161(2)
Fixed-effect model within subgroups
163(9)
Computational models
172(2)
Random effects with separate estimates of τ2
174(7)
Random effects with pooled estimate of τ2
181(8)
The proportion of variance explained
189(3)
Mixed-effects model
192(1)
Obtaining an overall effect in the presence of subgroups
193(2)
Summary points
195(2)
22 Meta-Regression
197(16)
Introduction
197(1)
Fixed-effect model
198(5)
Fixed or random effects for unexplained heterogeneity
203(3)
Random-effects model
206(6)
Summary points
212(1)
23 Notes On Subgroup Analyses And Meta-Regression
213(10)
Introduction
213(1)
Computational model
213(3)
Multiple comparisons
216(1)
Software
216(1)
Analyses of subgroups and regression analyses are observational
217(1)
Statistical power for subgroup analyses and meta-regression
218(1)
Summary points
219(4)
PART 6 PUTTING IT ALL IN CONTEXT
24 Looking At The Whole Picture
223(10)
Introduction
223(3)
Methylphenidate for adults with ADHD
226(2)
Impact of GLP-1 mimetics on blood pressure
228(1)
Augmenting clozapine with a second antipsychotic
228(3)
Conclusions
231(1)
Caveats
231(1)
Summary points
232(1)
25 Limitations Of The Random-Effects Model
233(10)
Introduction
233(1)
Assumptions of the random-effects model
234(1)
A textbook case
234(1)
When studies are pulled from the literature
235(2)
A useful fiction
237(1)
Transparency
238(1)
A narrowly defined universe
238(1)
Two important caveats
239(1)
In context
239(1)
Extreme cases
240(1)
Summary points
241(2)
26 Knapp-Hartung Adjustment
243(10)
Introduction
243(1)
Adjustment is rarely employed in simple analyses
243(1)
Adjusting the standard error
244(2)
The Knapp--Hartung adjustment for other effect size indices
246(1)
t distribution vs. Z distribution
247(1)
Limitations of the Knapp--Hartung adjustment
248(1)
Summary points
249(4)
PART 7 COMPLEX DATA STRUCTURES
27 Overview
253(2)
28 Independent Subgroups Within A Study
255(8)
Introduction
255(1)
Combining across subgroups
255(5)
Comparing subgroups
260(1)
Summary points
260(3)
29 Multiple Outcomes Or Time-Points Within A Study
263(14)
Introduction
263(1)
Combining across outcomes or time-points
264(6)
Comparing outcomes or time-points within a study
270(5)
Summary points
275(2)
30 Multiple Comparisons Within A Study
277(4)
Introduction
277(1)
Combining across multiple comparisons within a study
277(1)
Differences between treatments
278(1)
Summary points
279(2)
31 Notes On Complex Data Structures
281(6)
Introduction
281(1)
Summary effect
281(1)
Differences in effect
282(5)
PART 8 OTHER ISSUES
32 Overview
287(2)
33 Vote Counting -- A New Name For An Old Problem
289(6)
Introduction
289(1)
Why vote counting is wrong
290(1)
Vote counting is a pervasive problem
291(2)
Summary points
293(2)
34 Power Analysis For Meta-Analysis
295(18)
Introduction
295(1)
A conceptual approach
295(4)
In context
299(1)
When to use power analysis
300(1)
Planning for precision rather than for power
301(1)
Power analysis in primary studies
301(3)
Power analysis for meta-analysis
304(5)
Power analysis for a test of homogeneity
309(3)
Summary points
312(1)
35 Publication Bias
313(22)
Introduction
313(1)
The problem of missing studies
314(2)
Methods for addressing bias
316(1)
Illustrative example
317(1)
The model
317(1)
Getting a sense of the data
318(2)
Is there evidence of any bias?
320(1)
How much of an impact might the bias have?
320(4)
Summary of the findings for the illustrative example
324(1)
Conflating bias with the small-study effect
325(1)
Using logic to disentangle bias from small-study effects
326(1)
These methods do not give us the `correct' effect size
327(1)
Some important caveats
327(1)
Procedures do not apply to studies of prevalence
328(1)
The model for publication bias is simplistic
328(1)
Concluding remarks
329(1)
Putting it all together
330(1)
Summary points
330(5)
PART 9 ISSUES RELATED TO EFFECT SIZE
36 Overview
335(2)
37 Effect Sizes Rather Than p-Values
337(6)
Introduction
337(1)
Relationship between p-values and effect sizes
337(2)
The distinction is important
339(1)
The p-value is often misinterpreted
340(1)
Narrative reviews vs. meta-analyses
341(1)
Summary points
342(1)
38 Simpson's Paradox
343(6)
Introduction
343(1)
Circumcision and risk of HIV infection
343(2)
An example of the paradox
345(3)
Summary points
348(1)
39 Generality of the Basic Inverse-Variance Method
349(12)
Introduction
349(1)
Other effect sizes
350(3)
Other methods for estimating effect sizes
353(1)
Individual participant data meta-analyses
354(1)
Bayesian approaches
355(2)
Summary points
357(4)
PART 10 FURTHER METHODS
40 Overview
361(2)
41 Meta-Analysis Methods Based On Direction and p-Values
363(6)
Introduction
363(1)
Vote counting
363(1)
The sign test
363(1)
Combining p-values
364(4)
Summary points
368(1)
42 Further Methods For Dichotomous Data
369(8)
Introduction
369(1)
Mantel--Haenszel method
369(4)
One-step (Peto) formula for odds ratio
373(3)
Summary points
376(1)
43 Psychometric Meta-Analysis
377(14)
Introduction
377(1)
The attenuating effects of artifacts
378(2)
Meta-analysis methods
380(1)
Example of psychometric meta-analysis
381(3)
Comparison of artifact correction with meta-regression
384(1)
Sources of information about artifact values
384(1)
How heterogeneity is assessed
385(1)
Reporting in psychometric meta-analysis
386(1)
Concluding remarks
386(1)
Summary points
387(4)
PART 11 META-ANALYSIS IN CONTEXT
44 Overview
391(2)
45 When Does It Make Sense To Perform A Meta-Analysis?
393(8)
Introduction
393(1)
Are the studies similar enough to combine?
394(1)
Can I combine studies with different designs?
395(4)
How many studies are enough to carry out a meta-analysis?
399(1)
Summary points
400(1)
46 Reporting The Results Of A Meta-Analysis
401(6)
Introduction
401(1)
The computational model
402(1)
Forest plots
402(2)
Sensitivity analysis
404(1)
Summary points
405(2)
47 Cumulative Meta-Analysis
407(6)
Introduction
407(2)
Why perform a cumulative meta-analysis?
409(3)
Summary points
412(1)
48 Criticisms Of Meta-Analysis
413(12)
Introduction
413(1)
One number cannot summarize a research field
414(1)
The file drawer problem invalidates meta-analysis
414(1)
Mixing apples and oranges
415(1)
Garbage in, garbage out
416(1)
Important studies are ignored
417(1)
Meta-analysis can disagree with randomized trials
417(3)
Meta-analyses are performed poorly
420(1)
Is a narrative review better?
420(2)
Concluding remarks
422(1)
Summary points
422(3)
49 Comprehensive Meta-Analysis Software
425(18)
Introduction
425(1)
Features in CMA
426(1)
Teaching elements
427(1)
Documentation
427(1)
Availability
427(1)
Acknowledgments
427(1)
Motivating example
428(1)
Data entry
428(1)
Basic analysis
429(1)
What is the average effect size?
430(1)
How much does the effect size vary?
430(1)
Plot showing distribution of effects
431(1)
High-resolution plot
432(1)
Subgroup analysis
433(2)
Meta-regression
435(3)
Publication bias
438(1)
Explaining results
439(4)
50 How To Explain The Results Of An Analysis
443(28)
Introduction
443(1)
The overview
444(1)
The mean effect size
444(1)
Variation in effect size
444(1)
Notations
444(1)
Impact of resistance exercise on pain
445(5)
Correlation between letter knowledge and word recognition
450(5)
Statins for prevention of cardiovascular events
455(5)
Bupropion for smoking cessation
460(5)
Mortality following mitral-valve procedures in elderly patients
465(6)
PART 12 RESOURCES
51 Software For Meta-Analysis
471(2)
Comprehensive meta-analysis
471(1)
Metafor
471(1)
Stata
472(1)
Revman
472(1)
52 Web Sites, Societies, Journals, And Books
473(6)
Web sites
473(3)
Professional societies
476(1)
Journals
476(1)
Special issues dedicated to meta-analysis
477(1)
Books on systematic review methods and meta-analysis
477(2)
References 479(12)
Index 491