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Methods for Meta-Analysis in Medical Research [Hardback]

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(University of York, UK), (University of Leicester, UK), (University of Leicester, UK), (University of York, UK), (University of Leicester, UK)
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Methods for Meta-Analysis in Medical ResearchPalielināt
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Permanent link: https://www.kriso.lv/db/9780471490661.html
Keywords:
With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.
Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:
* Provides a thorough criticism and an up-to-date survey of meta-analysis methods
* Emphasises the practical approach, and illustrates the methods by numerous examples
* Describes the use of Bayesian methods in meta-analysis
* Includes discussion of appropriate software for each analysis
* Includes numerous references to more advanced treatment of specialist topics
* Refers to software code used in the examples available on the authors' Web site
Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

Recenzijas

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)

"This well-written book offers an exhaustive criticism and up-to-date references, illustrates effectively with real life examples and data" (Journal of Statistical Computation & Simulation, July 2004) "this is an excellent book..." (Short Book Reviews, April 2001)

"...recommended for mathematically skilled readers interested in getting an overview of the various methods and the existing literature..." (Statistics in Medicine, 15 October 2003) 

Preface xv
Acknowledgements xvii
Part A: Meta-Analysis Methodology: The Basics 1(160)
Introduction - Meta-analysis: Its Development and Uses
3(14)
Evidence-based health care
3(1)
Evidence-based everything!
4(1)
Pulling together the evidence - systematic reviews
5(3)
Why meta-analysis?
8(4)
Aim of this book
12(1)
Concluding remarks
13(4)
References
13(4)
Defining Outcome Measures used for Combining via Meta-analysis
17(20)
Introduction
17(1)
Non-comparative binary outcomes
18(2)
Odds
18(1)
Incidence rates
19(1)
Comparative binary outcomes
20(8)
The Odds ratio
20(3)
Relative risk (or rate ratio/relative rate)
23(2)
Risk differences between proportions (or the absolute risk reduction)
25(2)
The number needed to treat
27(1)
Comparisons of rates
28(1)
Other scales of measurement used in summarizing binary data
28(1)
Which scale to use?
28(1)
Continuous data
28(5)
Outcomes defined on their original metric (mean difference)
29(2)
Outcomes defined using standardized mean differences
31(2)
Ordinal outcomes
33(1)
Summary/Discussion
33(4)
References
34(3)
Assessing Between Study Heterogeneity
37(20)
Introduction
37(1)
Hypothesis tests for presence of heterogeneity
38(3)
Standard X2 test
38(1)
Extensions/alternative tests
39(1)
Example: Testing for heterogeneity in the cholesterol lowering trial dataset
40(1)
Graphical informal tests/explorations of heterogeneity
41(7)
Plot of normalized (z) scores
41(1)
Forest plot
42(4)
Radial plot (Galbraith diagram)
46(1)
L'Abbe plot
47(1)
Possible causes of heterogeneity
48(2)
Specific factors that may cause heterogeneity in RCTs
49(1)
Methods for investigating and dealing with sources of heterogeneity
50(3)
Change scale of outcome variable
51(1)
Include covariates in a regression model (meta-regression)
51(1)
Exclude studies
52(1)
Analyse groups of studies separately
52(1)
Use of random effects models
52(1)
Use of mixed-effect models
53(1)
The validity of pooling studies with heterogeneous outcomes
53(1)
Summary/Discussion
53(4)
References
54(3)
Fixed Effects Methods for Combining Study Estimates
57(16)
Introduction
57(1)
General fixed effect model - the inverse variance-weighted method
58(5)
Example: Combining odds ratios using the inverse variance-weighted method
59(3)
Example: Combining standardized mean differences using a continuous outcome scale
62(1)
Specific methods for combining odds ratios
63(7)
Mantel-Haenszel method for combining odds ratios
64(2)
Peto's method for combining odds ratios
66(2)
Combining odds ratios via maximum-likelihood techniques
68(1)
Exact methods of interval estimation
69(1)
Discussion of the relative merits of each method
69(1)
Summary/Discussion
70(3)
References
71(2)
Random Effects Models for Combining Study Estimates
73(14)
Introduction
73(1)
Algebraic derivation for random effects models by the weighted method
74(1)
Maximum likelihood and restricted maximum likelihood estimate solutions
75(1)
Comparison of estimation methods
76(1)
Example: Combining the cholesterol lowering trials using a random effects model
76(4)
Extensions to the random effects model
80(3)
Including uncertainty induced by estimating the between study variance
80(1)
Exact approach to random effects meta-analysis of binary data
81(1)
Miscellaneous extensions to the random effects model
82(1)
Comparison of random with fixed effect models
83(1)
Summary/Discussion
84(3)
References
84(3)
Exploring Between Study Heterogeneity
87(22)
Introduction
87(1)
Subgroup analyses
88(5)
Example: Stratification by study characteristics
89(1)
Example: Stratification by patient characteristics
89(4)
Regression models for meta-analysis
93(11)
Meta-regression models (fixed-effects regression)
93(2)
Meta-regression example: a meta-analysis of Bacillus Calmette-Guerin (BCG) vaccine for the prevention of tuberculosis (TB)
95(2)
Mixed effect models (random-effects regression)
97(2)
Mixed model example: A re-analysis of Bacillus Calmette-Guerin (BCG) vaccine for the prevention of tuberculosis (TB) trials
99(1)
Mixed modelling extensions
99(5)
Summary/Discussion
104(5)
References
105(4)
Publication Bias
109(24)
Introduction
109(1)
Evidence of publication and related bias
110(2)
Survey of authors
110(1)
Published versus registered trials in a meta-analysis
110(1)
Follow-up of cohorts of registered studies
111(1)
Non-empirical evidence
111(1)
Evidence of language bias
111(1)
The seriousness and consequences of publication bias for meta-analysis
112(1)
Predictors of publication bias (factors effecting the probability a study will get published)
112(1)
Identifying publication bias in a meta-analysis
112(7)
The funnel plot
113(3)
Rank correlation test
116(1)
Linear regression test
117(2)
Other methods to detect publication bias
119(1)
Practical advice on methods for detecting publication bias
119(1)
Taking into account publication bias or adjusting the results of a meta-analysis in the presence of publication bias
119(7)
Analysing only the largest studies
120(1)
Rosenthal's `file drawer' method
120(2)
Models which estimate the number of unpublished studies, but do not adjust
122(1)
Selection models using weighted distribution theory
123(1)
The `Trim and Fill' method
123(2)
The sensitivity approach of Copas
125(1)
Broader perspective solutions to publication bias
126(1)
Prospective registration of trials
126(1)
Changes in publication process and journals
126(1)
Including unpublished information
127(1)
Summary/Discussion
127(6)
References
128(5)
Study Quality
133(14)
Introduction
133(1)
Methodological factors that may affect the quality of studies
134(3)
Experimental studies
135(1)
Observational Studies
136(1)
Incorporating study quality into a meta-analysis
137(6)
Graphical plot
137(1)
Cumulative methods
138(1)
Regression model
138(2)
Weighting
140(2)
Excluding studies
142(1)
Sensitivity analysis
143(1)
Practical implementation
143(1)
Summary/Discussion
144(3)
References
144(3)
Sensitivity Analysis
147(6)
Introduction
147(1)
Sensitivity of results to inclusion criteria
147(3)
Sensitivity of results to meta-analytic methods
150(1)
Assessing the impact of choice of study weighting
150(1)
Summary/Discussion
151(2)
References
151(2)
Reporting the Results of a Meta-analysis
153(8)
Introduction
153(1)
Overview and structure of a report
154(1)
Graphical displays used for reporting the findings of a meta-analysis
155(3)
Forest plots
155(2)
Radial plots
157(1)
Funnel plots
157(1)
Displaying the distribution of effect size estimates
158(1)
Graphs investigating length of follow-up
158(1)
Summary/Discussion
158(3)
References
158(3)
Part B: Advanced and Specialized Meta-analysis Topics 161(140)
Bayesian Methods in Meta-analysis
163(28)
Introduction
163(1)
Bayesian methods in health research
163(6)
General introduction
163(3)
General advantages/disadvantages of Bayesian methods
166(1)
Example: Bayesian analysis of a single trial using a normal conjugate model
167(2)
Bayesian meta-analysis of normally distributed data
169(2)
Example: Combining trials with continuous outcome measures using Bayesian methods
171(1)
Bayesian meta-analysis of binary data
171(4)
Example: Combining binary outcome measures using Bayesian methods
173(2)
Empirical Bayes methods in meta-analysis
175(1)
Advantages/disadvantages of Bayesian methods in meta-analysis
176(3)
Advantages
176(2)
Disadvantages
178(1)
Extensions and specific areas of application
179(4)
Incorporating study quality
179(1)
Inclusion of covariates
180(1)
Model selection
180(1)
Hierarchical models
181(1)
Sensitivity analysis
181(1)
Comprehensive modelling
182(1)
Other developments
183(1)
Summary/Discussion
183(8)
References
183(8)
Meta-analysis of Individual Patient Data
191(8)
Introduction
191(2)
Procedural methodology
193(1)
Data collection
193(1)
Checking data
193(1)
Issues involved in carrying out IPD meta-analysis
193(1)
Comparing meta-analysis using IPD or summary data?
194(1)
Combining individual patient and summary data
195(1)
Summary/Discussion
196(3)
References
196(3)
Missing Data
199(6)
Introduction
199(1)
Reasons for missing data
200(1)
Categories of missing data at the study level
200(1)
Analytic methods for dealing with missing data
201(2)
General missing data methods which can be applied in the meta-analysis context
201(1)
Missing data methods specific to meta-analysis
202(1)
Example: Dealing with missing standard deviations of estimates in a meta-analysis
202(1)
Bayesian methods for missing data
203(1)
Summary/Discussion
203(2)
References
204(1)
Meta-analysis of Different Types of Data
205(24)
Introduction
205(1)
Combining ordinal data
205(1)
Issues concerning scales of measurement when combining data
206(3)
Transforming scales, maintaining same data type
207(1)
Binary outcome data reported on different scales
207(1)
Combining studies whose outcomes are reported using different data types
208(1)
Combining summaries of binary outcomes with those of continuous outcomes
208(1)
Non-parametric method of combining different data type effect measures
208(1)
Meta-analysis of diagnostic test accuracy
209(6)
Combining binary test results
209(6)
Combining ordered categorical test results
215(1)
Combining continuous test results
215(1)
Meta-analysis using surrogate markers
215(1)
Combining a number of cross-over trials using the patient preference outcome
216(1)
Vote-counting methods
217(1)
Combining p-values/significance levels
218(5)
Minimum p method
219(1)
Sum of z's method
220(1)
Sum of logs method
220(1)
Logit method
220(1)
Other methods of combining significance levels
220(1)
Appraisal of the methods
221(1)
Example of combining p-values
221(2)
Novel applications of meta-analysis using non-standard methods or data
223(1)
Summary/Discussion
223(6)
References
223(6)
Meta-analysis of Multiple and Correlated Outcome Measures
229(10)
Introduction
229(1)
Combining multiple p-values
230(1)
Method for reducing multiple outcomes to a single measure for each study
231(1)
Development of a multivariate model
231(5)
Model of Raudenbush et al.
231(1)
Model of Gleser and Olkin
232(1)
Multiple outcome model for clinical trials
232(1)
Random effect multiple outcome regression model
232(1)
DuMouchel's extended model for multiple outcomes
233(1)
Illustration of the use of multiple outcome models
233(3)
Summary/Discussion
236(3)
References
236(3)
Meta-analysis of Epidemiological and Other Observational Studies
239(20)
Introduction
239(1)
Extraction and derivation of study estimates
240(6)
Scales of measurement used to report and combine observational studies
243(1)
Data manipulation for data extraction
243(1)
Methods for transforming and adjusting reported results
244(2)
Analysis of summary data
246(2)
Heterogeneity of observational studies
246(1)
Fixed or random effects?
247(1)
Weighting of observational studies
247(1)
Methods for combining estimates of observational studies
247(1)
Dealing with heterogeneity and combining the OC and breast cancer studies
248(1)
Reporting the results of meta-analysis of observational studies
248(1)
Use of sensitivity and influence analysis
248(1)
Study quality considerations for observational studies
249(1)
Other issues concerning meta-analysis of observational studies
250(4)
Analysing individual patient data from observational studies
250(1)
Combining dose-reponse data
251(2)
Meta-analysis of single case research
253(1)
Unresolved issues concerning the meta-analysis of observational studies
254(1)
Summary/Discussion
255(4)
References
255(4)
Generalized Synthesis of Evidence-Combining Different Sources of Evidence
259(18)
Introduction
259(1)
Incorporating Single-arm studies: models for incorporating historical controls
259(3)
Example
260(2)
Combining matched and unmatched data
262(1)
Approaches for combining studies containing multiple and/or different treatment arms
263(2)
Approach of Gleser and Olkin
264(1)
Models of Berkey et al.
264(1)
Method of Higgins
264(1)
Mixed model of DuMouchel
264(1)
The confidence profile method
265(1)
Cross-design synthesis
266(7)
Beginnings
267(1)
Bayesian hierarchical models
267(4)
Grouped random effects models of Larose and Dey
271(1)
Synthesizing studies with disparate designs to assess the exposure effects on the incidence of a rare adverse event
271(1)
Combining the results of cancer studies in humans and other species
272(1)
Combining biochemical and epidemiological evidence
272(1)
Combining information from disparate toxicological studies using stratified ordinal regression
272(1)
Summary/Discussion
273(4)
References
273(4)
Meta-analysis of Survival Data
277(10)
Introduction
277(1)
Inferring/estimating and combining (log) hazard ratios
278(1)
Calculation of the `log-rank' odds ratio
278(1)
Calculation of pooled survival rates
279(1)
Method of Hunink and Wong
279(1)
Iterative generalized least squares for meta-analysis of survival data at multiple times
280(2)
Application of the model
281(1)
Identifying prognostic factors using a log (relative risk) measure
282(1)
Combining quality of life adjusted survival data
282(1)
Meta-analysis of survival data using individual patient data
283(1)
Pooling independent samples of survival data to form an estimator of the common survival function
283(1)
Is obtaining and using survival data necessary?
283(1)
Summary/Discussion
284(3)
References
284(3)
Cumulative Meta-analysis
287(8)
Introduction
287(1)
Example: Ordering by date of publication
288(2)
Using study characteristics other than date of publication
290(1)
Example: Ordering the cholesterol trials by baseline risk in the control group
290(1)
Bayesian approaches
291(1)
Issues regarding uses of cumulative meta-analysis
291(1)
Summary/Discussion
292(3)
References
292(3)
Miscellaneous and Developing Areas of Application in Meta-analysis
295(6)
Introduction
295(1)
Alternatives to conventional meta-analysis
295(2)
Estimating and extrapolating a response surface
295(1)
Odd man out method
296(1)
Best evidence synthesis
296(1)
Developing areas
297(4)
Prospective meta-analysis
297(1)
Economic evaluation through meta-analysis
298(1)
Combining meta-analysis and decision analysis
299(1)
Net benefit model synthesizing disparate sources of information
299(1)
References
299(2)
Appendix I: Software Used for the Examples in this Book 301(8)
Subject index 309


Alex Sutton has published extensively on meta-analysis methodology generally, and on publication bias specifically in recent years, including a major systematic review on the topic of the methodology that has been developed for meta-analysis. He currently has an active interest in the area of partially reported study information, which is currently under-researched. Dr. Sutton is co-author of a textbook on metaanalysis (Methods for Meta Analysis in Medical Research), which was published by Wiley in 2000.