Introduction to meta-analysis:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Chichester
Wiley
2009
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XXVIII, 421 S. graph. Darst. |
| ISBN: | 9780470057247 |
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MARC
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| 245 | 1 | 0 | |a Introduction to meta-analysis |c Michael Borenstein ... |
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|---|---|
| adam_text | Contents
List of Tables
xiii
List of Figures
xv
Acknowledgements
xix
Preface
xxi
Web site
xxix
PART
1:
INTRODUCTION
1
HOW A META-ANALYSIS WORKS
3
Introduction
3
Individual studies
3
The summary effect
5
Heterogeneity of effect sizes
6
Summary points
7
2
WHY PERFORM A META-ANALYSIS
9
Introduction
9
The streptokinase
meta-
analysis
10
Statistical significance
11
Clinical importance of the effect
12
Consistency of effects
12
Summary points
J
4
PART
2:
EFFECT SIZE AND PRECISION
3
OVERVIEW
17
Treatment effects and effect sizes
17
Parameters and estimates
18
Outline of effect size computations
19
4
EFFECT SIZES BASED ON MEANS
21
Introduction
21
Raw (unstandardized) mean difference
D
21
Standardized mean difference,
d
and
g
25
Response ratios
30
Summary points
32
5
EFFECT
SIZES BASED ON BINARY DATA
(2x2
TABLES)
33
Introduction
33
Risk ratio
34
Odds ratio
36
Risk difference
37
Choosing an effect size index
38
Summary points
39
6
EFFECT SIZES BASED ON CORRELATIONS
41
Introduction
41
Computing
r
41
Other approaches
43
Summary points
43
7
CONVERTING AMONG EFFECT SIZES
45
Introduction
45
Converting from the log odds ratio to
d
47
Converting from
d
to the log odds ratio
47
Converting from
r
to
d
48
Converting from
d
to
r
48
Summary points
49
8
FACTORS THAT AFFECT PRECISION
51
Introduction
51
Factors that affect precision
52
Sample size
52
Study design
53
Summary points
55
9
CONCLUDING REMARKS
57
PART
3:
FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
10
OVERVIEW
61
Introduction
61
Nomenclature
62
11
FIXED-EFFECT MODEL
63
Introduction
63
The true effect size
63
Impact of sampling error
63
Performing a fixed-effect meta-analysis
65
Summary points
67
12
RANDOM-EFFECTS MODEL
69
Introduction
69
The true effect sizes
69
Impact of sampling error
70
Performing a random-effects meta-analysis
72
Summary points
74
13
FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
77
Introduction
77
Definition of a summary effect
77
Estimating the summary effect
78
Extreme effect size in a large study or a small study
79
Confidence interval
80
The null hypothesis
83
Which model should we use?
83
Model should not be based on the test for heterogeneity
84
Concluding remarks
85
Summary points
85
14
WORKED EXAMPLES (PART
1) 87
Introduction
87
Worked example for continuous data (Part
1) 87
Worked example for binary data (Part
1) 92
Worked example for correlational data (Part
1) 97
Summary points
102
PART
4:
HETEROGENEITY
15
OVERVIEW 10S
Introduction
105
Nomenclature
106
Worked examples
106
16
IDENTIFYING AND QUANTIFYING HETEROGENEITY
107
Introduction
107
Isolating the variation in true effects
107
Computing
Q
109
Estimating
r2
114
The
ƒ
2 statistic
117
Comparing the measures of heterogeneity
119
Confidence intervals for r2
122
Confidence intervals (or uncertainty intervals) for I2
124
Summary points
125
17
PREDICTION INTERVALS
127
Introduction
127
Prediction intervals in primary studies
127
Prediction intervals in meta-analysis
129
Confidence intervals and prediction intervals
131
Comparing the confidence interval with the prediction interval
132
Summary points
133
18
WORKED EXAMPLES (PART
2) 135
Introduction
135
Worked example for continuous data (Part
2) 135
Worked example for binary data (Part
2) 139
Worked example for correlational data (Part
2) 143
Summary points
147
19
SUBGROUP ANALYSES
149
Introduction
149
Fixed-effect model within subgroups
151
Computational models
161
Random effects with separate estimates of
τ 2
164
Random effects with pooled estimate of
τ
171
The proportion of variance explained
179
Mixed-effects model
183
Obtaining an overall effect in the presence of subgroups
184
Summary points
186
20
META-REGRESSION
187
Introduction
187
Fixed-effect model
188
Fixed or random effects for unexplained heterogeneity
193
Random-effects model
196
Summary points
203
21
NOTES ON SUBGROUP ANALYSES AND META-REGRESSION
205
Introduction
205
Computational model
205
Multiple comparisons
208
Software
209
Analyses of subgroups and regression analyses are observational
209
Statistical power for subgroup analyses and meta-regression
210
Summary points
211
PART
5:
COMPLEX DATA STRUCTURES
22
OVERVIEW
215
23
INDEPENDENT SUBGROUPS WITHIN A STUDY
217
Introduction
217
Combining across subgroups
218
Comparing subgroups
222
Summary points
223
24
MULTIPLE OUTCOMES OR TIME-POINTS WITHIN A STUDY
225
Introduction
225
Combining across outcomes or time-points
226
Comparing outcomes or time-points within a study
233
Summary points
238
25
MULTIPLE COMPARISONS WITHIN A STUDY
239
Introduction
239
Combining across multiple comparisons within a study
239
Differences between treatments
240
Summary points
241
26
NOTES ON COMPLEX DATA STRUCTURES Mi
Introduction
243
Summary effect
243
Differences in effect
244
PART
6:
OTHER ISSUES
27
OVERVIEW
249
28
VOTE COUNTING
-
A NEW NAME FOR AN OLD PROBLEM 2S1
Introduction
251
Why vote counting is wrong
252
Vote counting is a pervasive problem
253
Summary points
255
29
POWER ANALYSIS FOR
МЕТА
-ANALYSIS
257
Introduction
257
A conceptual approach
257
In context
261
When to use power analysis
262
Planning
for precision rather than for power
263
Power analysis in primary studies
263
Power analysis for meta-analysis
267
Power analysis for a test of homogeneity
272
Summary points
275
30
PUBLICATION BIAS
277
Introduction
277
The problem of missing studies
278
Methods for addressing bias
280
Illustrative example
281
The model
281
Getting a sense of the data
281
Is there evidence of any bias?
283
Is the entire effect an artifact of bias?
284
How much of an impact might the bias have?
286
Summary of the findings for the illustrative example
289
Some important caveats
290
Small-study effects
291
Concluding remarks
291
Summary points
291
PART
7:
ISSUES RELATED TO EFFECT SIZE
31
OVERVIEW
295
32
EFFECT SIZES RATHER THAN p-VALUES
297
Introduction
297
Relationship between p-values and effect sizes
297
The distinction is important
299
The p-value is often misinterpreted
300
Narrative reviews vs. meta-analyses
301
Summary points
302
33
SIMPSON S PARADOX
303
Introduction
303
Circumcision and risk of
HIV
infection
303
An example of the paradox
305
Summary points
308
34
GENERALITY OF THE BASIC INVERSE-VARIANCE METHOD
311
Introduction
311
Other effect sizes
312
Other methods for estimating effect sizes
315
Individual participant data meta-analyses
316
В
ay
esian
approaches
318
Summary points
319
PART
8:
FURTHER METHODS
35
OVERVIEW
323
36
МЕТА
-ANALYSIS METHODS BASED ON DIRECTION AND p-VALUES
325
Introduction
325
Vote counting
325
The sign test
325
Combining p- values
326
Summary points
330
37
FURTHER METHODS FOR DICHOTOMOUS DATA
331
Introduction
331
Mantel-Haenszel method
331
One-step
(Peto)
formula for odds ratio
336
Summary points
339
38
PSYCHOMETRIC META-ANALYSIS
341
Introduction
341
The attenuating effects of artifacts
342
Meta-analysis methods
344
Example of psychometric meta-analysis
346
Comparison of artifact coiTection with meta-regression
348
Sources of information about artifact values
349
How heterogeneity is assessed
349
Reporting in psychometric meta-analysis
350
Concluding remarks
351
Summary points
351
PART
9:
МЕТА
-ANALYSIS IN CONTEXT
39
OVERVIEW
Ж
40
WHEN DOES IT MAKE SENSE TO PERFORM A
МЕТА
-ANALYSIS? 3S7
Introduction
357
Are the studies similar enough to combine?
358
Can I combine studies with different designs?
359
How many studies are enough to carry out a meta-analysis?
363
Summary points
364
41
REPORTING THE RESULTS OF A META-ANALYSIS
365
Introduction
365
The computational model
366
Forest plots
366
Sensitivity analysis
368
Summary points
369
42
CUMULATIVE META-ANALYSIS
371
Introduction
371
Why perform a cumulative meta-analysis?
373
Summary points
376
43
CRITICISMS OF META-ANALYSIS
377
Introduction
377
One number cannot summarize a research field
378
The file drawer problem invalidates meta-analysis
378
Mixing apples and oranges
379
Garbage in, garbage out
380
Important studies are ignored
381
Meta-analysis can disagree with randomized trials
381
Meta-analyses are performed poorly
384
Is a narrative review better?
385
Concluding remarks
386
Summary points
386
PART
10:
RESOURCES AND SOFTWARE
44
SOFTWARE
391
Introduction
391
The software
392
Three examples of meta-analysis software
393
Comprehensive Meta-Analysis (CMA)
2.0 395
RevMan
5.0 398
Stata
macros with
Stata
10.0 400
Summary points
403
45
BOOKS, WEB SITES AND PROFESSIONAL ORGANIZATIONS
405
Books on systematic review methods
405
Books on meta-analysis
405
Web sites
406
REFERENCES
409
INDEX
415
This book provides a clear and thorough introduction to meta-analysis, the process of
synthesizing data from a series of separate studies. Meta-analysis has become a critically
important tool in fields as diverse as medicine, pharmacology, epidemiology, education,
psychology, business, and ecology. Introduction to Meta-Analysis:
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
Clarifies concepts using text and figures, followed by formulas and examples
Explains how to avoid common mistakes in meta-analysis
Discusses controversies in meta-analysis
Features a web site with additional material and exercises
A superb combination of lucid prose and informative graphics, written by four of the world s
leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and
Rothstein
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, who used pre-publication versions of some of the chapters, 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.
|
| adam_txt |
Contents
List of Tables
xiii
List of Figures
xv
Acknowledgements
xix
Preface
xxi
Web site
xxix
PART
1:
INTRODUCTION
1
HOW A META-ANALYSIS WORKS
3
Introduction
3
Individual studies
3
The summary effect
5
Heterogeneity of effect sizes
6
Summary points
7
2
WHY PERFORM A META-ANALYSIS
9
Introduction
9
The streptokinase
meta-
analysis
10
Statistical significance
11
Clinical importance of the effect
12
Consistency of effects
12
Summary points
J
4
PART
2:
EFFECT SIZE AND PRECISION
3
OVERVIEW
17
Treatment effects and effect sizes
17
Parameters and estimates
18
Outline of effect size computations
19
4
EFFECT SIZES BASED ON MEANS
21
Introduction
21
Raw (unstandardized) mean difference
D
21
Standardized mean difference,
d
and
g
25
Response ratios
30
Summary points
32
5
EFFECT
SIZES BASED ON BINARY DATA
(2x2
TABLES)
33
Introduction
33
Risk ratio
34
Odds ratio
36
Risk difference
37
Choosing an effect size index
38
Summary points
39
6
EFFECT SIZES BASED ON CORRELATIONS
41
Introduction
41
Computing
r
41
Other approaches
43
Summary points
43
7
CONVERTING AMONG EFFECT SIZES
45
Introduction
45
Converting from the log odds ratio to
d
47
Converting from
d
to the log odds ratio
47
Converting from
r
to
d
48
Converting from
d
to
r
48
Summary points
49
8
FACTORS THAT AFFECT PRECISION
51
Introduction
51
Factors that affect precision
52
Sample size
52
Study design
53
Summary points
55
9
CONCLUDING REMARKS
57
PART
3:
FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
10
OVERVIEW
61
Introduction
61
Nomenclature
62
11
FIXED-EFFECT MODEL
63
Introduction
63
The true effect size
63
Impact of sampling error
63
Performing a fixed-effect meta-analysis
65
Summary points
67
12
RANDOM-EFFECTS MODEL
69
Introduction
69
The true effect sizes
69
Impact of sampling error
70
Performing a random-effects meta-analysis
72
Summary points
74
13
FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
77
Introduction
77
Definition of a summary effect
77
Estimating the summary effect
78
Extreme effect size in a large study or a small study
79
Confidence interval
80
The null hypothesis
83
Which model should we use?
83
Model should not be based on the test for heterogeneity
84
Concluding remarks
85
Summary points
85
14
WORKED EXAMPLES (PART
1) 87
Introduction
87
Worked example for continuous data (Part
1) 87
Worked example for binary data (Part
1) 92
Worked example for correlational data (Part
1) 97
Summary points
102
PART
4:
HETEROGENEITY
15
OVERVIEW 10S
Introduction
105
Nomenclature
106
Worked examples
106
16
IDENTIFYING AND QUANTIFYING HETEROGENEITY
107
Introduction
107
Isolating the variation in true effects
107
Computing
Q
109
Estimating
r2
114
The
ƒ
2 statistic
117
Comparing the measures of heterogeneity
119
Confidence intervals for r2
122
Confidence intervals (or uncertainty intervals) for I2
124
Summary points
125
17
PREDICTION INTERVALS
127
Introduction
127
Prediction intervals in primary studies
127
Prediction intervals in meta-analysis
129
Confidence intervals and prediction intervals
131
Comparing the confidence interval with the prediction interval
132
Summary points
133
18
WORKED EXAMPLES (PART
2) 135
Introduction
135
Worked example for continuous data (Part
2) 135
Worked example for binary data (Part
2) 139
Worked example for correlational data (Part
2) 143
Summary points
147
19
SUBGROUP ANALYSES
149
Introduction
149
Fixed-effect model within subgroups
151
Computational models
161
Random effects with separate estimates of
τ 2
164
Random effects with pooled estimate of
τ
171
The proportion of variance explained
179
Mixed-effects model
183
Obtaining an overall effect in the presence of subgroups
184
Summary points
186
20
META-REGRESSION
187
Introduction
187
Fixed-effect model
188
Fixed or random effects for unexplained heterogeneity
193
Random-effects model
196
Summary points
203
21
NOTES ON SUBGROUP ANALYSES AND META-REGRESSION
205
Introduction
205
Computational model
205
Multiple comparisons
208
Software
209
Analyses of subgroups and regression analyses are observational
209
Statistical power for subgroup analyses and meta-regression
210
Summary points
211
PART
5:
COMPLEX DATA STRUCTURES
22
OVERVIEW
215
23
INDEPENDENT SUBGROUPS WITHIN A STUDY
217
Introduction
217
Combining across subgroups
218
Comparing subgroups
222
Summary points
223
24
MULTIPLE OUTCOMES OR TIME-POINTS WITHIN A STUDY
225
Introduction
225
Combining across outcomes or time-points
226
Comparing outcomes or time-points within a study
233
Summary points
238
25
MULTIPLE COMPARISONS WITHIN A STUDY
239
Introduction
239
Combining across multiple comparisons within a study
239
Differences between treatments
240
Summary points
241
26
NOTES ON COMPLEX DATA STRUCTURES Mi
Introduction
243
Summary effect
243
Differences in effect
244
PART
6:
OTHER ISSUES
27
OVERVIEW
249
28
VOTE COUNTING
-
A NEW NAME FOR AN OLD PROBLEM 2S1
Introduction
251
Why vote counting is wrong
252
Vote counting is a pervasive problem
253
Summary points
255
29
POWER ANALYSIS FOR
МЕТА
-ANALYSIS
257
Introduction
257
A conceptual approach
257
In context
261
When to use power analysis
262
Planning
for precision rather than for power
263
Power analysis in primary studies
263
Power analysis for meta-analysis
267
Power analysis for a test of homogeneity
272
Summary points
275
30
PUBLICATION BIAS
277
Introduction
277
The problem of missing studies
278
Methods for addressing bias
280
Illustrative example
281
The model
281
Getting a sense of the data
281
Is there evidence of any bias?
283
Is the entire effect an artifact of bias?
284
How much of an impact might the bias have?
286
Summary of the findings for the illustrative example
289
Some important caveats
290
Small-study effects
291
Concluding remarks
291
Summary points
291
PART
7:
ISSUES RELATED TO EFFECT SIZE
31
OVERVIEW
295
32
EFFECT SIZES RATHER THAN p-VALUES
297
Introduction
297
Relationship between p-values and effect sizes
297
The distinction is important
299
The p-value is often misinterpreted
300
Narrative reviews vs. meta-analyses
301
Summary points
302
33
SIMPSON'S PARADOX
303
Introduction
303
Circumcision and risk of
HIV
infection
303
An example of the paradox
305
Summary points
308
34
GENERALITY OF THE BASIC INVERSE-VARIANCE METHOD
311
Introduction
311
Other effect sizes
312
Other methods for estimating effect sizes
315
Individual participant data meta-analyses
316
В
ay
esian
approaches
318
Summary points
319
PART
8:
FURTHER METHODS
35
OVERVIEW
323
36
МЕТА
-ANALYSIS METHODS BASED ON DIRECTION AND p-VALUES
325
Introduction
325
Vote counting
325
The sign test
325
Combining p- values
326
Summary points
330
37
FURTHER METHODS FOR DICHOTOMOUS DATA
331
Introduction
331
Mantel-Haenszel method
331
One-step
(Peto)
formula for odds ratio
336
Summary points
339
38
PSYCHOMETRIC META-ANALYSIS
341
Introduction
341
The attenuating effects of artifacts
342
Meta-analysis methods
344
Example of psychometric meta-analysis
346
Comparison of artifact coiTection with meta-regression
348
Sources of information about artifact values
349
How heterogeneity is assessed
349
Reporting in psychometric meta-analysis
350
Concluding remarks
351
Summary points
351
PART
9:
МЕТА
-ANALYSIS IN CONTEXT
39
OVERVIEW
Ж
40
WHEN DOES IT MAKE SENSE TO PERFORM A
МЕТА
-ANALYSIS? 3S7
Introduction
357
Are the studies similar enough to combine?
358
Can I combine studies with different designs?
359
How many studies are enough to carry out a meta-analysis?
363
Summary points
364
41
REPORTING THE RESULTS OF A META-ANALYSIS
365
Introduction
365
The computational model
366
Forest plots
366
Sensitivity analysis
368
Summary points
369
42
CUMULATIVE META-ANALYSIS
371
Introduction
371
Why perform a cumulative meta-analysis?
373
Summary points
376
43
CRITICISMS OF META-ANALYSIS
377
Introduction
377
One number cannot summarize a research field
378
The file drawer problem invalidates meta-analysis
378
Mixing apples and oranges
379
Garbage in, garbage out
380
Important studies are ignored
381
Meta-analysis can disagree with randomized trials
381
Meta-analyses are performed poorly
384
Is a narrative review better?
385
Concluding remarks
386
Summary points
386
PART
10:
RESOURCES AND SOFTWARE
44
SOFTWARE
391
Introduction
391
The software
392
Three examples of meta-analysis software
393
Comprehensive Meta-Analysis (CMA)
2.0 395
RevMan
5.0 398
Stata
macros with
Stata
10.0 400
Summary points
403
45
BOOKS, WEB SITES AND PROFESSIONAL ORGANIZATIONS
405
Books on systematic review methods
405
Books on meta-analysis
405
Web sites
406
REFERENCES
409
INDEX
415
This book provides a clear and thorough introduction to meta-analysis, the process of
synthesizing data from a series of separate studies. Meta-analysis has become a critically
important tool in fields as diverse as medicine, pharmacology, epidemiology, education,
psychology, business, and ecology. Introduction to Meta-Analysis:
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
Clarifies concepts using text and figures, followed by formulas and examples
Explains how to avoid common mistakes in meta-analysis
Discusses controversies in meta-analysis
Features a web site with additional material and exercises
A superb combination of lucid prose and informative graphics, written by four of the world's
leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and
Rothstein
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, who used pre-publication versions of some of the chapters, 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. |
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| spelling | Introduction to meta-analysis Michael Borenstein ... 1. publ. Chichester Wiley 2009 XXVIII, 421 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Meta-analysis Meta-Analysis as Topic Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Metaanalyse (DE-588)4169552-5 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Metaanalyse (DE-588)4169552-5 s Statistische Analyse (DE-588)4116599-8 s 1\p DE-604 Borenstein, Michael Sonstige (DE-588)1043133577 oth Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016824939&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016824939&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Introduction to meta-analysis Meta-analysis Meta-Analysis as Topic Statistische Analyse (DE-588)4116599-8 gnd Metaanalyse (DE-588)4169552-5 gnd |
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| title | Introduction to meta-analysis |
| title_auth | Introduction to meta-analysis |
| title_exact_search | Introduction to meta-analysis |
| title_exact_search_txtP | Introduction to meta-analysis |
| title_full | Introduction to meta-analysis Michael Borenstein ... |
| title_fullStr | Introduction to meta-analysis Michael Borenstein ... |
| title_full_unstemmed | Introduction to meta-analysis Michael Borenstein ... |
| title_short | Introduction to meta-analysis |
| title_sort | introduction to meta analysis |
| topic | Meta-analysis Meta-Analysis as Topic Statistische Analyse (DE-588)4116599-8 gnd Metaanalyse (DE-588)4169552-5 gnd |
| topic_facet | Meta-analysis Meta-Analysis as Topic Statistische Analyse Metaanalyse Einführung |
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