Advances in meta-analysis:
Gespeichert in:
1. Verfasser: | |
---|---|
Format: | Buch |
Sprache: | English |
Veröffentlicht: |
New York [u.a.]
Springer
2012
|
Schriftenreihe: | Statistics for social and behavorial sciences
|
Schlagworte: | |
Online-Zugang: | Inhaltsverzeichnis |
Beschreibung: | XIII, 155 S. graph. Darst. 24 cm |
ISBN: | 9781461422778 1461422779 |
Internformat
MARC
LEADER | 00000nam a2200000zc 4500 | ||
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020 | |a 9781461422778 |c (hbk.) £53.99 |9 978-1-461-42277-8 | ||
020 | |a 1461422779 |c (hbk.) £53.99 |9 1-461-42277-9 | ||
035 | |a (OCoLC)802342991 | ||
035 | |a (DE-599)HBZHT017160964 | ||
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100 | 1 | |a Pigott, Therese D. |e Verfasser |0 (DE-588)1076326587 |4 aut | |
245 | 1 | 0 | |a Advances in meta-analysis |c Terri D. Pigott |
264 | 1 | |a New York [u.a.] |b Springer |c 2012 | |
300 | |a XIII, 155 S. |b graph. Darst. |c 24 cm | ||
336 | |b txt |2 rdacontent | ||
337 | |b n |2 rdamedia | ||
338 | |b nc |2 rdacarrier | ||
490 | 0 | |a Statistics for social and behavorial sciences | |
650 | 0 | 7 | |a Metaanalyse |0 (DE-588)4169552-5 |2 gnd |9 rswk-swf |
689 | 0 | 0 | |a Metaanalyse |0 (DE-588)4169552-5 |D s |
689 | 0 | |5 DE-604 | |
776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |o 10.1007/978-1-4614-2278-5 |z 978-1-4614-2278-5 |
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999 | |a oai:aleph.bib-bvb.de:BVB01-024824430 |
Datensatz im Suchindex
_version_ | 1804148943776382976 |
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adam_text | Contents
1
Introduction
.............................................................. 1
1.1
Background
......................................................... 1
1.2
Planning
a Systematic Review
...................................... 2
1.3
Analyzing Complex Data from a Meta-analysis
................... 4
1.4
Interpreting Results from a Meta-analysis
.......................... 4
1.5
What Do Readers Need to Know to Use This Book?
.............. 5
References
................................................................. 6
2
Review of Effect Sizes
................................................... 7
2.1
Background
......................................................... 7
2.2
Introduction to Notation and Basic Meta-analysis
................. 7
2.3
The Random Effects Mean and Variance
.......................... 8
2.4
Common Effect Sizes Used in Examples
........................... 10
2.4.1
Standardized Mean Difference
.............................. 10
2.4.2
Correlation Coefficient
...................................... 10
2.4.3
Log Odds Ratio
............................................. 11
References
................................................................. 12
3
Planning a Meta-analysis in a Systematic Review
.................... 13
3.1
Background
......................................................... 13
3.2
Deciding on Important Moderators of Effect Size
................. 14
3.3
Choosing Among Fixed, Random
and Mixed Effects Models
.......................................... 16
3.4
Computing the Variance Component in Random
and Mixed Models
.................................................. 18
3.4.1
Example
..................................................... 20
3.5
Confounding of Moderators in Effect Size Models
................ 21
3.5.1
Example
..................................................... 23
3.6
Conducting a Meta-Regression
..................................... 25
3.6.1
Example
..................................................... 25
3.7
Interpretation of Moderator Analyses
.............................. 28
References
................................................................. 32
í
Contents
4
Power
Analysis for the Mean Effect Size
.............................. 35
4.1
Background
......................................................... 35
4.2
Fundamentals of Power Analysis
................................... 37
4.3
Test of the Mean Effect Size in the Fixed Effects Model
.......... 39
4.3.1
Z-Test for the Mean Effect Size
in the Fixed Effects Model
.................................. 39
4.3.2
The Power of the Test of the Mean Effect Size
in Fixed Effects Models
..................................... 41
4.3.3
Deciding on Values for Parameters
to Compute Power
.......................................... 42
4.3.4
Example: Computing the Power of the Test
of the Mean
................................................. 43
4.3.5
Example: Computing the Number of Studies Needed
to Detect an Important Fixed Effects Mean
................ 45
4.3.6
Example: Computing the Detectable Fixed Effects
Mean in a Meta-analysis
.................................... 46
4.4
Test of the Mean Effect Size in the Random Effects Model
....... 47
4.4.1
The Power of the Test of the Mean Effect Size
in Random Effects Models
.................................. 48
4.4.2
Positing a Value for
τ2
for Power Computations
in the Random Effects Model
............................... 49
4.4.3
Example: Estimating the Power of the Random
Effects Mean
................................................ 50
4.4.4
Example: Computing the Number of Studies
Needed to Detect an Important Random Effect Mean
..... 51
4.4.5
Example: Computing the Detectable Random
Effects Mean in a Meta-analysis
............................ 52
References
................................................................. 53
5
Power for the Test of Homogeneity in Fixed
and Random Effects Models
............................................ 55
5.1
Background
......................................................... 55
5.2
The Test of Homogeneity of Effect Sizes
in a Fixed Effects Model
............................................ 56
5.2.1
The Power of the Test of Homogeneity
in a Fixed Effects Model
.................................... 56
5.2.2
Choosing Values for the Parameters Needed
to Compute Power of the Homogeneity Test
in Fixed Effects Models
..................................... 57
5.2.3
Example: Estimating the Power of the
Test of Homogeneity in Fixed Effects Models
............. 58
Contents
5.3
The Test of the Significance of the Variance Component
in Random Effects Models
.......................................... 59
5.3.1
Power of the Test of the Significance of the Variance
Component in Random Effects Models
.................... 60
5.3.2
Choosing Values for the Parameters Needed to Compute
the Variance Component in Random Effects Models
...... 61
5.3.3
Example: Computing Power for Values of
τ2,
the Variance Component
.................................... 62
References
................................................................. 66
Power Analysis for Categorical Moderator
Models of Effect Size
.................................................... 67
6.1
Background
......................................................... 67
6.2
Categorical Models of Effect Size: Fixed Effects
One-Way
ANO VA
Models
......................................... 68
6.2.1
Tests in a Fixed Effects One-Way
ANOVA
Model
........ 68
6.2.2
Power of the Test of Between-Group
Homogeneity, Qb, in Fixed Effects Models
................ 68
6.2.3
Choosing Parameters for the Power of Qb in Fixed
Effects Models
.............................................. 70
6.2.4
Example: Power of the Test of Between-Group
Homogeneity in Fixed Effects Models
..................... 70
6.2.5
Power of the Test of Within-Group Homogeneity,
Qw, in Fixed Effects Models
................................ 71
6.2.6
Choosing Parameters for the Test of Qw in Fixed
Effects Models
.............................................. 72
6.2.7
Example: Power of the Test of Within-Group
Homogeneity in Fixed Effects Models
..................... 73
6.3
Categorical Models of Effect Size: Random Effects
One-Way
ANO VA
Models
......................................... 74
6.3.1
Power of Test of Between-Group Homogeneity
in the Random Effects Model
............................... 74
6.3.2
Choosing Parameters for the Test of Between-Group
Homogeneity in Random Effects Models
.................. 76
6.3.3
Example: Power of the Test of Between-Group
Homogeneity in Random Effects Models
.................. 76
6.4
Linear Models of Effect Size (Meta-regression)
................... 78
References
................................................................. 78
Missing Data in Meta-analysis: Strategies and Approaches
......... 79
7.1
Background
......................................................... 79
7.2
Missing Studies in a Meta-analysis
................................. 80
7.2.1
Identification of Publication Bias
........................... 80
7.2.2
Assessing the Sensitivity of Results
to Publication Bias
.......................................... 82
Contents
7.3
Missing Effect Sizes in a Meta-analysis
........................... 85
7.4
Missing Moderators in Effect Size Models
........................ 86
7.5
Theoretical Basis for Missing Data Methods
...................... 87
7.5.1
Multivariate Normality in Meta-analysis
.................. 88
7.5.2
Missing Data Mechanisms or Reasons
for Missing Data
........................................... 89
7.6
Commonly Used Methods for Missing Data
in Meta-analysis
.................................................... 90
7.6.1
Complete-Case Analysis
................................... 90
7.6.2
Available Case Analysis or Pairwise Deletion
............ 92
7.6.3
Single Value Imputation with the
Complete Case Mean
...................................... 93
7.6.4
Single Value Imputation Using
Regression Techniques
..................................... 95
7.7
Model-Based Methods for Missing Data in Meta-analysis
....... 97
7.7.1
Maximum-Likelihood Methods for Missing
Data Using the EM Algorithm
............................. 97
7.7.2
Multiple Imputation for Multivariate Normal Data
....... 99
References
................................................................ 106
Including Individual Participant Data in Meta-analysis
............ 109
8.1
Background
........................................................ 109
8.2
The Potential for IPD Meta-analysis
.............................. 110
8.3
The Two-Stage Method for a Mix of IPD and AD
................ 112
8.3.1
Simple Random Effects Models
with Aggregated Data
...................................... 112
8.3.2
Two-Stage Estimation with Both Individual Level
and Aggregated Data
....................................... 114
8.4
The One-Stage Method for a Mix of IPD and AD
................ 115
8.4.1
IPD Model for the Standardized Mean Difference
........ 115
8.4.2
IPD Model for the Correlation
............................. 116
8.4.3
Model for the One-Stage Method
with Both IPD and AD
..................................... 116
8.5
Effect Size Models with Moderators Using a Mix
of IPD and AD
..................................................... 118
8.5.1
Two-Stage Methods for Meta-regression
with a Mix of IPD and AD
................................ 119
8.5.2
One-Stage Method for Meta-regression
with a Mix of IPD and AD
................................ 120
8.5.3
Meta-regression for IPD Data Only
....................... 121
8.5.4
One-Stage Meta-regression with a Mix
of IPD and AD
............................................. 121
References
................................................................ 130
Contents xiii
9
Generalizations from Meta-analysis
.................................. 133
9.1
Background
....................................................... 133
9.1.1
The Preventive Health Services
(2009)
Report
on Breast Cancer Screening
.............................. 134
9.1.2
The National Reading Panel s Meta-analysis
on Learning to Read
...................................... 135
9.2
Principles of Generalized Causal Inference
...................... 135
9.2.1
Surface Similarity
......................................... 135
9.2.2
Ruling Out Irrelevancies
.................................. 136
9.2.3
Making Discriminations
.................................. 137
9.2.4
Interpolation and Extrapolation
........................... 138
9.2.5
Causal Explanation
........................................ 138
9.3
Suggestions for Generalizing from a Meta-analysis
............. 139
References
................................................................ 140
10
Recommendations for Producing a High Quality
Meta-analysis
........................................................... 143
10.1
Background
....................................................... 143
10.2
Understanding the Research Problem
............................ 143
10.3
Having an a Priori Plan for the Meta-analysis
................... 144
10.4
Carefully and Thoroughly Interpret the Results
of Meta-analysis
.................................................. 145
References
................................................................ 146
11
Data Appendix
.......................................................... 147
11.1
Sirin
(2005)
Meta-analysis on the Association
Between Measures of Socioeconomic Status
and Academic Achievement
...................................... 147
11.2 Hackshaw et
al.
(1997)
Meta-analysis on Exposure
to Passive Smoking and Lung Cancer
............................ 149
11.3
Eagly
et al.
(2003)
Meta-analysis on Gender Differences
in Transformational Leadership
.................................. 151
References
................................................................ 152
Index
.......................................................................... 153
|
any_adam_object | 1 |
author | Pigott, Therese D. |
author_GND | (DE-588)1076326587 |
author_facet | Pigott, Therese D. |
author_role | aut |
author_sort | Pigott, Therese D. |
author_variant | t d p td tdp |
building | Verbundindex |
bvnumber | BV039966807 |
classification_rvk | CM 3400 ST 650 |
ctrlnum | (OCoLC)802342991 (DE-599)HBZHT017160964 |
discipline | Informatik Psychologie |
format | Book |
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id | DE-604.BV039966807 |
illustrated | Illustrated |
indexdate | 2024-07-10T00:15:10Z |
institution | BVB |
isbn | 9781461422778 1461422779 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024824430 |
oclc_num | 802342991 |
open_access_boolean | |
owner | DE-11 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-83 DE-521 |
owner_facet | DE-11 DE-473 DE-BY-UBG DE-19 DE-BY-UBM DE-83 DE-521 |
physical | XIII, 155 S. graph. Darst. 24 cm |
publishDate | 2012 |
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publishDateSort | 2012 |
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series2 | Statistics for social and behavorial sciences |
spelling | Pigott, Therese D. Verfasser (DE-588)1076326587 aut Advances in meta-analysis Terri D. Pigott New York [u.a.] Springer 2012 XIII, 155 S. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Statistics for social and behavorial sciences Metaanalyse (DE-588)4169552-5 gnd rswk-swf Metaanalyse (DE-588)4169552-5 s DE-604 Erscheint auch als Online-Ausgabe 10.1007/978-1-4614-2278-5 978-1-4614-2278-5 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024824430&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | Pigott, Therese D. Advances in meta-analysis Metaanalyse (DE-588)4169552-5 gnd |
subject_GND | (DE-588)4169552-5 |
title | Advances in meta-analysis |
title_auth | Advances in meta-analysis |
title_exact_search | Advances in meta-analysis |
title_full | Advances in meta-analysis Terri D. Pigott |
title_fullStr | Advances in meta-analysis Terri D. Pigott |
title_full_unstemmed | Advances in meta-analysis Terri D. Pigott |
title_short | Advances in meta-analysis |
title_sort | advances in meta analysis |
topic | Metaanalyse (DE-588)4169552-5 gnd |
topic_facet | Metaanalyse |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024824430&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
work_keys_str_mv | AT pigotttheresed advancesinmetaanalysis |