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Introduction to Meta-Analysis [Kõva köide]

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(Medical Research Council, UK), (Northwestern University, US), (Baruch College, New York, New York), (Biostat Inc, New Jersey)
  • Formaat: Hardback, 452 pages, kõrgus x laius x paksus: 254x179x31 mm, kaal: 925 g
  • Ilmumisaeg: 13-Mar-2009
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470057246
  • ISBN-13: 9780470057247
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Introduction to Meta-AnalysisSuurem pilt
  • Formaat: Hardback, 452 pages, kõrgus x laius x paksus: 254x179x31 mm, kaal: 925 g
  • Ilmumisaeg: 13-Mar-2009
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470057246
  • ISBN-13: 9780470057247
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470057247.html
Märksõnad:
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.

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 

Introduction to Meta-Analysis is an excellent resource for novices and experts alike. The book provides a clear and comprehensive presentation of all basic and most advanced approaches to meta-analysis. This book will be referenced for decades. Michael A. McDaniel, Professor of Human Resources and Organizational Behavior, Virginia Commonwealth University

Arvustused

"Both books can be recommended for graduate training and are useful additions to the library of those interested in the meta-analytic accumulation of literatures on training, vocational learning, and education in the professions." (Vocations and Learning, 15 December 2010)

List of Tables
xiii
List of Figures
xv
Acknowledgements xix
Preface xxi
Web site xxix
PART 1: INTRODUCTION
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)
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
12(1)
Consistency of effects
12(2)
Summary points
14(3)
PART 2: EFFECT SIZE AND PRECISION
Overview
17(4)
Treatment effects and effect sizes
17(1)
Parameters and estimates
18(1)
Outline of effect size computations
19(2)
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(2)
Summary points
32(1)
Effect Sizes Based on Binary Data (2*2 Tables)
33(8)
Introduction
33(1)
Risk ratio
34(2)
Odds ratio
36(1)
Risk difference
37(1)
Choosing an effect size index
38(1)
Summary points
39(2)
Effect Sizes Based on Correlations
41(4)
Introduction
41(1)
Computing r
41(2)
Other approaches
43(1)
Summary points
43(2)
Converting Among Effect Sizes
45(6)
Introduction
45(2)
Converting from the log odds ratio to d
47(1)
Converting from d to the log odds ratio
47(1)
Converting from r to d
48(1)
Converting from d to r
48(1)
Summary points
49(2)
Factors That Affect Precision
51(6)
Introduction
51(1)
Factors that affect precision
52(1)
Sample size
52(1)
Study design
53(2)
Summary points
55(2)
Concluding Remarks
57(4)
PART 3: FIXED-EFFECT VERSUS RANDOM-EFFECTS MODELS
Overview
61(1)
Introduction
61(1)
Nomenclature
62(5)
Fixed-Effect Model
63(1)
Introduction
63(1)
The true effect size
63(1)
Impact of sampling error
63(2)
Performing a fixed-effect meta-analysis
65(2)
Summary points
67(10)
Random-Effects Model
69(1)
Introduction
69(1)
The true effect sizes
69(1)
Impact of sampling error
70(2)
Performing a random-effects meta-analysis
72(2)
Summary points
74(3)
Fixed-Effect Versus Random-Effects Models
77(10)
Introduction
77(1)
Definition of a summary effect
77(1)
Estimating the summary effect
78(1)
Extreme effect size in a large study or a small study
79(1)
Confidence interval
80(3)
The null hypothesis
83(1)
Which model should we use?
83(1)
Model should not be based on the test for heterogeneity
84(1)
Concluding remarks
85(1)
Summary points
85(2)
Worked Examples (Part 1)
87(18)
Introduction
87(1)
Worked example for continuous data (Part 1)
87(5)
Worked example for binary data (Part 1)
92(5)
Worked example for correlational data (Part 1)
97(5)
Summary points
102(3)
PART 4: HETEROGENEITY
Overview
105(2)
Introduction
105(1)
Nomenclature
106(1)
Worked examples
106(1)
Identifying and Quantifying Heterogeneity
107(20)
Introduction
107(1)
Isolating the variation in true effects
107(2)
Computing Q
109(5)
Estimating T2
114(3)
The I2 statistic
117(2)
Comparing the measures of heterogeneity
119(3)
Confidence intervals for T2
122(2)
Confidence intervals (or uncertainty intervals) for I2
124(1)
Summary points
125(2)
Prediction Intervals
127(8)
Introduction
127(1)
Prediction intervals in primary studies
127(2)
Prediction intervals in meta-analysis
129(2)
Confidence intervals and prediction intervals
131(1)
Comparing the confidence interval with the prediction interval
132(1)
Summary points
133(2)
Worked Examples (Part 2)
135(14)
Introduction
135(1)
Worked example for continuous data (Part 2)
135(4)
Worked example for binary data (Part 2)
139(4)
Worked example for correlational data (Part 2)
143(4)
Summary points
147(2)
Subgroup Analyses
149(38)
Introduction
149(2)
Fixed-effect model within subgroups
151(10)
Computational models
161(3)
Random effect with separate estimates of T2
164(7)
Random effect with pooled estimate of T2
171(8)
The proportion of variance explained
179(4)
Mixed-effect model
183(1)
Obtaining an overall effect in the presence of subgroups
184(2)
Summary points
186(1)
Meta-Regression
187(18)
Introduction
187(1)
Fixed-effect model
188(5)
Fixed or random effects for unexplained heterogeneity
193(3)
Random-effects model
196(7)
Summary points
203(2)
Notes on Subgroup Analyses and Meta-Regression
205(10)
Introduction
205(1)
Computational model
205(3)
Multiple comparison
208(1)
Software
209(1)
Analyses of subgroups and regression analyses are observational
209(1)
Statistical power for subgroup analyses and meta-regression
210(1)
Summary points
211(4)
PART 5: COMPLEX DATA STRUCTURES
Overview
215(2)
Independent Subgroups Within a Study
217(8)
Introduction
217(1)
Combining across subgroups
218(4)
Comparing subgroups
222(1)
Summary points
223(2)
Multiple Outcomes or Time-Points Within a Study
225(14)
Introduction
225(1)
Combining across outcomes or time-points
226(7)
Comparing outcomes or time-points within a study
233(5)
Summary points
238(1)
Multiple Comparisons Within a Study
239(4)
Introduction
239(1)
Combining across multiple comparisons within a study
239(1)
Differences between treatments
240(1)
Summary points
241(2)
Notes on Complex Data Structures
243(6)
Introduction
243(1)
Summary effect
243(1)
Differences in effect
244(5)
PART 6: OTHER ISSUES
Overview
249(2)
Vote Counting - A New Name For an Old Problem
251(6)
Introduction
251(1)
Why vote counting is wrong
252(1)
Vote counting is a pervasive problem
253(2)
Summary points
255(2)
Power Analysis for Meta-Analysis
257(20)
Introduction
257(1)
A conceptual approach
257(4)
In context
261(1)
When to use power analysis
262(1)
Planning for precision rather than for power
263(1)
Power analysis in primary studies
263(4)
Power analysis for meta-analysis
267(5)
Power analysis for a test of homogeneity
272(3)
Summary points
275(2)
Publication Bias
277(18)
Introduction
277(1)
The problem of missing studies
277(3)
Methods for addressing bias
280(1)
Illustrative example
281(1)
The model
281(1)
Getting a sense of the data
281(2)
Is there evidence of any bias?
283(1)
Is the entire effect an artifact of bias?
284(2)
How much of an impact might the bias have?
286(3)
Summary of the findings for the illustrative example
289(1)
Some important caveats
290(1)
Small-study effects
291(1)
Concluding remarks
291(1)
Summary points
291(4)
PART 7: ISSUES RELATED TO EFFECT SIZE
Overview
295(2)
Effect sizes Rather than p-Values
297(6)
Introduction
297(1)
Relationship between p-values and effect sizes
297(2)
The distinction is important
299(1)
The p-value is often misinterpreted
300(1)
Narrative reviews vs. meta-analyses
301(1)
Summary points
302(1)
Simpson's Paradox
303(8)
Introduction
303(1)
Circumcision and risk of HIV infection
303(2)
An example of the paradox
305(3)
Summary points
308(3)
Generality of the Basic Inverse-variance Method
311(12)
Introduction
311(1)
Other effect sizes
312(3)
Other methods for estimating effect sizes
315(1)
Individual participant data meta-analyses
316(2)
Bayesian approaches
318(1)
Summary points
319(4)
PART 8: FURTHER METHODS
Overview
323(2)
Meta-Analysis Methods Based on Direction and p-Values
325(6)
Introduction
325(1)
Vote counting
325(1)
The sign test
325(1)
Combining p-values
326(4)
Summary points
330(1)
Further Methods for Dichotomous Data
331(10)
Introduction
331(1)
Mantel-Haenszel method
331(5)
One-step (Peto) formula for odds ratio
336(3)
Summary points
339(2)
Psychometric Meta-Analysis
341(14)
Introduction
341(1)
The attenuating effects of artifacts
342(2)
Meta-analysis methods
344(2)
Example of psychometric meta-analysis
346(2)
Comparison of artifact correction with meta-regression
348(1)
Sources of information about artifact values
349(1)
How heterogeneity is assessed
349(1)
Reporting in psychometric meta-analysis
350(1)
Concluding remarks
351(1)
Summary points
351(4)
PART 9: META-ANALYSIS IN CONTEXT
Overview
355(2)
When Does it Make Sense to Perform a Meta-Analysis?
357(8)
Introduction
357(1)
Are the studies similar enough to combine?
358(1)
Can I combine studies with different designs?
359(4)
How many studies are enough to carry out a meta-analysis?
363(1)
Summary points
364(1)
Reporting The Results of a Meta-Analysis
365(6)
Introduction
365(1)
The computational model
366(1)
Forest plots
366(2)
Sensitivity analysis
368(1)
Summary points
369(2)
Cumulative Meta-Analysis
371(6)
Introduction
371(2)
Why perform a cumulative meta-analysis?
373(3)
Summary points
376(1)
Criticisms of Meta-Analysis
377(14)
Introduction
377(1)
One number cannot summarize a research field
378(1)
The file drawer problem invalidates meta-analysis
378(1)
Mixing apples and oranges
379(1)
Garbage in, garbage out
380(1)
Impotant studies are ignored
381(1)
Meta-analysis can disagree with randomized trials
381(3)
Meta-analysis are performed poorly
384(1)
Is a narrative review better?
385(1)
Concluding remarks
386(1)
Summary points
386(5)
PART 10: RESOURCES AND SOFTWARE
Software
391(14)
Introduction
391(1)
The software
392(1)
Three examples of meta-analysis software
393(2)
Comprehensive Meta-Analysis (CMA) 2.0
395(3)
RevMan 5.0
398(2)
Stata macros with Stata 10.0
400(3)
Summary points
403(2)
Books, Web Sites and Professional Organizations
405(4)
Books on systematic review methods
405(1)
Books on meta-analysis
405(1)
Web sites
406(3)
References 409(6)
Index 415
Michael Borenstein, Director of Biostatistical Programming Associates Professor Borenstein is the co-editor of the recently published Wiley book Publication Bias in Meta-Analysis, and has taught dozens of workshops on meta-analysis. He also helped to develop the best-selling software programs for statistical power analysis.

Hannah Rothstein, Zicklin School of Business, Baruch College Professor Rothstein teaches regular seminars on meta-analysis and systematic reviews, and has 20 years of active research in the area of meta-analysis. She has authored several meta-analyses as well as articles on methodological issues in the area, and made numerous presentations on the topic. Having contributed chapters to two books on meta-analysis, she co-edited Publication Bias in Meta-Analysis.

Larry Hedges, University of Chicago A pioneer in meta-analysis, Professor Hedges has published over 80 papers in the area (many describing techniques he himself developed, that are now used as standard), co-edited the Handbook for Synthesis Research, and co-authored three books on the topic including the seminal Statistical Methods for Meta-Analysis. He has also taught numerous short courses on meta-analysis sponsored by various international organizations such as the ASA.

Julian Higgins, MRC Biostatistics Unit, Cambridge Dr Higgins has published many methodological papers in meta-analysis. He works closely with the Cochrane Collaboration and is an editor of the Cochrane Handbook. He has much experience of teaching meta-analysis, both at Cambridge University and, by invitation, around the world.