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E-raamat: Systematic Reviews in Health Research: Meta-Analysis in Context

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Systematic Reviews in Health Research

Explore the cutting-edge of systematic reviews in healthcare

In this Third Edition of the classic Systematic Reviews textbook, now titled Systematic Reviews in Health Research, a team of distinguished researchers deliver a comprehensive and authoritative guide to the rapidly evolving area of systematic reviews and meta-analysis. The book demonstrates why systematic reviewswhen conducted properlyprovide the highest quality evidence on clinical and public health interventions and shows how they contribute to inference in many other contexts. The new edition reflects the broad role of systematic reviews, including:





Twelve new chapters, covering additional study designs, methods and software, for example, on genetic association studies, prediction models, prevalence studies, network and dose-response meta-analysis Thorough update of 15 chapters focusing on systematic reviews of interventions Access to a companion website offering supplementary materials and practical exercises (www.systematic-reviews3.org)

A key text for health researchers, Systematic Reviews in Health Research is also an indispensable resource for practitioners, students, and instructors in the health sciences needing to understand research synthesis.
Preface xix
Tribute xxi
List of Contributors
xxviii
About the Companion Website xxxiii
Chapter 1 Systematic Reviews in Health Research: An Introduction
1(16)
Matthias Egger
Julian P.T. Higgins
George Davey Smith
1.1 Systematic Review, Meta-Analysis, or Evidence Synthesis?
1(2)
1.2 The Scope of Meta-Analysis
3(1)
1.3 Historical Notes
3(2)
1.4 Why do we Need Systematic Reviews? The Situation in the 1980s
5(1)
1.5 Traditional Reviews
6(1)
1.6 Limitations of a Single Study
7(1)
1.7 A More Transparent and Thorough Appraisal
8(1)
1.8 The Epidemiology of Results
8(1)
1.9 What was the Evidence in 1981?
9(2)
1.10 An Exercise in Mega-Silliness?
11(1)
1.11 Conclusions
12(5)
PART I Principles and Procedures
17(112)
Chapter 2 Principles of Systematic Reviewing
19(17)
Julian P.T. Higgins
George Davey Smith
Douglas G. Altman
Matthias Egger
2.1 Developing a Review Protocol
19(4)
2.1.1 Objectives and Eligibility Criteria
21(1)
2.1.2 Literature Search
22(1)
2.1.3 Selection of Studies, Assessment of Methodological Quality, and Data Extraction
23(1)
2.2 Presenting, Combining, and Interpreting Results
23(8)
2.2.1 Standardized Outcome Measure
26(1)
2.2.2 Graphical Display
26(1)
2.2.3 Heterogeneity Between Study Results
27(2)
2.2.4 Methods for Estimating A Typical Effect
29(1)
2.2.5 Sensitivity Analysis
30(1)
2.3 Interpreting Findings
31(2)
2.4 Conclusions
33(3)
Chapter 3 Identifying Randomized Controlled Trials
36(19)
Julie Glanville
Carol Lefebvre
3.1 Searching CENTRAL to Identify Randomized Controlled Trials
37(4)
3.1.1 Where Do CENTRAL Records Come From?
40(1)
3.1.2 The MEDLINE Re-tagging Project
40(1)
3.1.3 The Embase Projects
40(1)
3.1.4 Crowdsourcing and Other Initiatives
40(1)
3.2 Sources to Search in Addition to CENTRAL
41(3)
3.2.1 Key Sources of Systematic Reviews
42(1)
3.2.2 Databases and Other Sources Not (Yet) Searched for Inclusion in CENTRAL
43(1)
3.2.3 Trials Registers, Regulatory Agency Sources, and Clinical Study Reports
43(1)
3.3 Searching for Studies Other than Randomized Controlled Trials
44(1)
3.4 Building Search Strategies
45(4)
3.4.1 Identifying the Concepts in the Review Question
45(1)
3.4.2 Identifying the Search Terms to Capture the Concepts
46(1)
3.4.3 Combining Search Terms and Concepts
47(1)
3.4.4 When to Stop Searching
48(1)
3.5 Conclusions
49(6)
Chapter 4 Assessing the Risk of Bias in Randomized Trials
55(19)
Matthew J. Page
Douglas G. Altman
Matthias Egger
4.1 Risk of Bias and Quality
55(1)
4.2 The Evidence Base for Risk of Bias
56(1)
4.3 Sources of Bias in Randomized Trials
57(5)
4.3.1 Bias Arising from the Randomization Process
57(2)
4.3.2 Bias Due to Deviations from the Intended Interventions
59(1)
4.3.3 Bias Due to Missing Outcome Data
60(1)
4.3.4 Bias in Measurement of Outcomes
61(1)
4.3.5 Bias Due to Selective Reporting
62(1)
4.4 Approaches to Assessing Risk of Bias in Randomized Trials
62(5)
4.4.1 Composite Scale Approach
62(1)
4.4.2 Domain-Based Approach
63(1)
4.4.2.1 Cochrane Risk of Bias Tool for Randomized Trials
63(3)
4.4.2.2 RoB 2
66(1)
4.5 Incorporating Risk of Bias in Meta-Analysis
67(1)
4.5.1 Excluding Studies at High Risk of Bias from the Meta-Analysis
67(1)
4.5.2 Quality Score as a Weight in Meta-Analysis
67(1)
4.5.3 Adjusting Results of Trials for Bias
68(1)
4.6 Conclusions
68(6)
Chapter 5 Investigating and Dealing with Publication Bias and Other Reporting Biases
74(17)
Matthew J. Page
Jonathan A.C. Sterne
Julian P.T. Higgins
Matthias Egger
5.1 The Evidence Base for Reporting Biases in Health Research
75(2)
5.2 Approaches to Minimize Risk of Bias Due to Missing Results
77(1)
5.2.1 Searching Beyond Journal Articles
77(1)
5.2.2 Restricting Meta-Analyses to Inception Cohorts
77(1)
5.3 Approaches to Assess Risk of Bias Due to Missing Results
78(7)
5.3.1 Tools to Assess Selective Nonreporting of Results in The Identified Studies
79(1)
5.3.2 Qualitative Signals for Additional Missing Results
79(1)
5.3.3 Funnel Plots
80(2)
5.3.4 Contour-Enhanced Funnel Plots
82(2)
5.3.5 Tests for Funnel Plot Asymmetry
84(1)
5.3.6 Sensitivity Analyses
85(1)
5.3.7 Summary of Approaches
85(1)
5.4 Conclusions
85(6)
Chapter 6 Managing People and Data
91(18)
Eliane Rohner
Julia Bohlius
Bruno R. da Costa
Sven Trelle
6.1 The Team
91(3)
6.1.1 Composition and Roles
91(2)
6.1.2 Training
93(1)
6.1.3 Project Management and Coordination
93(1)
6.1.4 Communication
94(1)
6.2 The Data
94(11)
6.2.1 Types and Structure of Data in a Systematic Review
94(1)
6.2.2 Reference Management and Eligibility Assessment
95(1)
6.2.3 Data Extraction from Component Studies
96(1)
6.2.3.1 Development and Piloting of Data Extraction Forms
96(2)
6.2.3.2 Implementation of Data Extraction Forms
98(2)
6.2.3.3 Data Extraction Process
100(1)
6.2.4 Risk of Bias Assessment
101(1)
6.2.5 Derivation or Approximation of Data
101(1)
6.2.5.1 Continuous Outcomes
101(1)
6.2.5.2 Binary Outcomes
102(1)
6.2.5.3 Time-to-Event Outcomes
102(1)
6.2.5.4 Extracting Data from Graphs
103(2)
6.3 Outlook: Automation and Data Sharing
105(4)
Chapter 7 Reporting and Appraisal of Systematic Reviews
109(20)
Larissa Shamseer
Beverley Shea
Brian Hutton
David Moher
7.1 Consequences of Poor Reporting
110(1)
7.2 Reporting Systematic Review Protocols
110(1)
7.3 Reporting Systematic Reviews
111(10)
7.3.1 Reviews of Health Interventions
111(1)
7.3.1.1 The PRISMA Guideline
111(1)
7.3.1.2 The PRISMA Checklist
111(4)
7.3.1.3 The PRISMA Flow Diagram
115(3)
7.3.1.4 Extensions of the PRISMA Statement
118(1)
7.3.1.5 Synthesizing Review Interventions
118(3)
7.4 Reporting Systematic Reviews Without Meta-Analyses
121(1)
7.5 Other guidance for Reporting Systematic Reviews
121(1)
7.5.1 Institute of Medicine Standards for Reporting Systematic Reviews
121(1)
7.5.2 MECIR and MECCIR Standards
121(1)
7.6 Reporting Other Types of Systematic Reviews
122(1)
7.6.1 Meta-Analyses of Observational Studies
122(1)
7.6.2 Reviews of Experimental Animal Studies
122(1)
7.6.3 Reviews of Qualitative Research
122(1)
7.7 Optimizing Reporting in Practice
123(1)
7.8 Appraisal of Systematic Reviews
123(2)
7.8.1 ROBIS: Risk of Bias in Systematic Reviews
124(1)
7.8.2 AMSTAR: A MeaSurement Tool to Assess Systematic Reviews
125(1)
7.9 Conclusions
125(4)
PART II Meta-Analysis
129(142)
Chapter 8 Effect Measures
131(28)
Julian P.T. Higgins
Jonathan J. Deeks
Douglas G. Altman
8.1 Individual Study Estimates of Intervention Effect: Binary Outcomes
131(6)
8.1.1 Computations
134(2)
8.1.2 What is the Event?
136(1)
8.2 Individual Study Estimates of Intervention Effect: Continuous Outcomes
137(2)
8.3 Individual Study Estimates of Intervention Effect: Time-to-Event Outcomes
139(1)
8.4 Individual Study Estimates of Intervention Effect: Rates
140(1)
8.5 Individual Study Estimates of Intervention Effect: Ordinal Outcomes
141(1)
8.6 Criteria for Selection of a Summary Statistic
141(8)
8.6.1 Consistency of Effects Across Studies
142(1)
8.6.1.1 The L'Abbe Plot
143(1)
8.6.1.2 Empirical Evidence of Consistency
144(2)
8.6.2 Mathematical Properties
146(1)
8.6.3 Issues in Interpretation of Effect Measures
147(2)
8.7 Case Studies
149(5)
8.8 Discussion
154(5)
Chapter 9 Combining Results Using Meta-Analysis
159(26)
Jonathan J. Deeks
Richard D. Riley
Julian P.T. Higgins
9.1 Meta-Analysis
159(2)
9.1.1 General Principles
159(1)
9.1.2 Heterogeneity
160(1)
9.1.3 Summary Statistics for Intervention Effects
161(1)
9.2 Formulae for Deriving a Summary Estimate of the Intervention Effect by Combining Trial Results (Meta-Analysis)
161(9)
9.2.1 Fixed-Effect and Random-Effects Methods
161(1)
9.2.2 Fixed-Effect Meta-Analysis using the Inverse-Variance Method
162(2)
9.2.3 Mantel--Haenszel Methods for Binary Outcomes
164(2)
9.2.4 Peto's Odds Ratio Method
166(1)
9.2.5 Extending the Peto Method for Combining Time-to-Event Data
166(1)
9.2.6 Random-Effects Meta-Analysis using the Inverse-Variance Method
167(2)
9.2.7 Other Random-Effects Meta-Analysis Methods
169(1)
9.3 Confidence Interval for Overall Effect
170(1)
9.4 Test Statistic for Overall Effect
170(2)
9.5 Prediction Interval for the Intervention Effect in a New Trial
172(2)
9.6 Meta-Analysis with Individual Participant Data
174(1)
9.7 Additional Analyses
174(1)
9.8 Some Practical Issues
175(1)
9.9 Discussion
175(10)
Chapter 10 Exploring Heterogeneity
185(19)
Julian P.T. Higgins
Tianjing Li
10.1 Clinical, Methodological, and Statistical Variability Across Studies
186(2)
10.2 Real and Spurious Heterogeneity
188(1)
10.3 Subgroup Analysis: Dividing the Evidence into Subsets
189(4)
10.3.1 Between-Study and Within-Study Subgroups
190(1)
10.3.2 Investigating Within-Study Variability through Meta-Analysis
191(1)
10.3.3 Examining Differences between Subgroups
192(1)
10.4 Meta-Regression
193(5)
10.4.1 Meta-Regression: Technicalities
193(3)
10.4.2 Subgroup Analysis is a Special Case of Meta-Regression
196(1)
10.4.3 Proportion of Variance Explained
197(1)
10.4.4 Extensions to Meta-Regression
197(1)
10.5 Practical Problems in the Exploration of Heterogeneity
198(2)
10.5.1 Correlated Predictor Variables and Causal Conclusions
198(1)
10.5.2 Aggregating Participant-Level Predictor Variables at the Study Level
198(1)
10.5.3 Spurious Findings and Undetected Associations
199(1)
10.5.4 Statistical Artefacts when Investigating Small-Study Effects and Underlying Risk
199(1)
10.6 Closing Remarks
200(4)
Chapter 11 Dealing with Missing Outcome Data in Meta-Analysis
204(16)
Ian R. White
Dimitris Mavridis
11.1 Analysis of a Single Study with Missing Data
204(1)
11.2 Meta-Analysis with Missing Data
205(5)
11.2.1 Understand the Extent of Missing Data in Each Included Study
207(2)
11.2.2 Understand How the Missing Data were Handled in Each Published Report
209(1)
11.2.3 Evaluate the Risk of Bias Due to Missing Data in Each Published Report
209(1)
11.2.4 Perform Alternative Analyses Exploring the Impact of the Missing Data under Different Assumptions
209(1)
11.3 Method 1: Using Reasons for Missing Data and Simple Assumptions
210(1)
11.4 Method 2: Quantifying Departures from MAR
211(1)
11.5 Two Worked Examples
212(4)
11.5.1 Haloperidol Meta-Analysis
212(1)
11.5.2 Mirtazapine Meta-Analysis
213(3)
11.6 Recommendations
216(4)
Chapter 12 Individual Participant Data Meta-Analysis
220(18)
Mark C. Simmonds
Lesley A. Stewart
12.1 Advantages and Challenges of Collecting Individual Participant Data
222(1)
12.1.1 Access to Additional Outcome Data
222(1)
12.1.2 Data in a Consistent and Usable Format
222(1)
12.1.3 More Choice of Analysis Options
223(1)
12.1.4 Ability to Examine Individual-Level Characteristics
223(1)
12.1.5 Challenges in Using Individual Participant Data
223(1)
12.2 Performing a Systematic Review Using Individual Participant Data
223(2)
12.2.1 Planning the Review and Identifying Studies
223(1)
12.2.2 Obtaining Individual Participant Data
224(1)
12.2.3 Checking and Cleaning the Data
225(1)
12.3 Methods for Meta-Analysis with Individual Participant Data
225(5)
12.3.1 Two-Stage Approaches for Overall Effect
226(1)
12.3.1.1 Example: The PARIS Review (Part 1)
226(1)
12.3.2 One-Stage Approaches for Overall Effect
227(1)
12.3.2.1 Example: The PARIS Review (Part 2)
228(1)
12.3.3 Time-to-Event Analysis
228(1)
12.3.3.1 Example: Chemotherapy for Non-Small Cell Lung Cancer
229(1)
12.4 Going Beyond Estimating the Summary Effect
230(1)
12.4.1 Two-Stage Approaches for Investigating Covariates
230(1)
12.4.2 One-Stage Approaches for Investigating Covariates
231(1)
12.4.2.1 Example: The PARIS Review (Part 3)
231(1)
12.5 Individual Participant Data Meta-Analysis of Observational Studies
231(3)
12.5.1 Example: Aortic Pulse Wave Velocity and Cardiovascular Disease
233(1)
12.6 Combining Individual Participant Data with Published Data
234(1)
12.7 Reporting Findings
234(1)
12.8 Conclusion
234(4)
Chapter 13 Network Meta-Analysis
238(20)
Georgia Salanti
Julian P.T. Higgins
13.1 Indirect Comparison and Transitivity
238(2)
13.2 Indirect and Direct Evidence
240(3)
13.3 Network Plots of Interventions
243(1)
13.4 Systematic Reviews Underlying Network Meta-Analysis
244(1)
13.5 Synthesis of Data
245(3)
13.5.1 Assumptions About Heterogeneity
246(1)
13.5.2 Statistical Methods
246(2)
13.6 Intransitivity and Inconsistency
248(2)
13.7 Ranking Interventions
250(2)
13.8 Conclusions
252(6)
Chapter 14 Dose-Response Meta-Analysis
258(13)
Nicola Orsini
Susanna C. Larsson
Georgia Salanti
14.1 Example: Coffee Consumption and Mortality Risk
259(1)
14.2 Estimating Dose--Response Association Within a Study
259(1)
14.3 A Linear Trend for a Single Study
259(1)
14.4 A Quadratic Trend for a Single Study
260(1)
14.5 A Restricted Cubic Spline Model for a Single Study
261(1)
14.6 Synthesizing Dose--Response Association Across Studies
262(3)
14.7 Testing Departure From a Linear Dose--Response Relationship
265(1)
14.8 Extensions, Limitations, and Developments
266(1)
14.9 Conclusions
267(4)
PART III Specific Study Designs
271(142)
Chapter 15 Systematic Reviews of Nonrandomized Studies of Interventions
273(23)
Jelena Savovid
Penny F. Whiting
Olaf M. Dekkers
15.1 The Importance of Nonrandomized Studies in the Evaluation of Interventions
274(1)
15.2 Defining the Research Question and Eligibility Criteria for the Review
275(4)
15.2.1 Specifying PICO in Reviews of Nonrandomized Studies of Interventions
275(1)
15.2.2 Defining Types of Nonrandomized Study to Include
276(3)
15.3 Searching for Nonrandomized Studies of Interventions
279(1)
15.4 Risk of Bias
280(7)
15.4.1 Confounding
280(2)
15.4.2 Selection Bias
282(1)
15.4.3 Information Bias
283(1)
15.4.4 Reporting Bias
284(1)
15.4.5 Assessing Risk of Bias in Nonrandomized Studies of Interventions Included in a Systematic Review: A Domain-Based Approach
284(3)
15.5 Synthesizing Results
287(4)
15.5.1 Exploring Heterogeneity
287(1)
15.5.2 The Role of Meta-Analysis
287(2)
15.5.3 Combining Results from Randomized and Nonrandomized Studies
289(2)
15.6 Conclusions
291(5)
Chapter 16 Systematic Reviews of Diagnostic Accuracy
296(28)
Yemisi Takwoingi
Jonathan J. Deeks
16.1 Rationale for Undertaking Systematic Reviews of Studies of Test Accuracy
297(1)
16.2 Features of Studies of Test Accuracy
297(1)
16.3 Summary Measures of Diagnostic Accuracy
298(1)
16.3.1 Types of Data
298(1)
16.4 Measures of Diagnostic Accuracy
298(3)
16.5 Systematic Reviews of Studies of Diagnostic Accuracy
301(2)
16.5.1 Literature Searching
301(1)
16.5.2 Assessment of Methodological Quality
302(1)
16.6 Meta-Analysis of Studies of Diagnostic Accuracy
303(1)
16.7 General Principles of Diagnostic Accuracy Meta-Analysis
304(2)
16.8 Methods for Meta-Analysis of a Single Test
306(2)
16.8.1 Estimation of a Summary Sensitivity and Specificity at a Common Threshold
306(1)
16.8.2 Estimation of an SROC Curve
307(1)
16.9 Quantifying and Investigating Heterogeneity
308(2)
16.9.1 Comparison of Summary Points
309(1)
16.9.2 Comparison of Summary Curves
310(1)
16.10 Comparisons of the Accuracy of Two or More Tests
310(3)
16.10.1 Test Comparison Strategy
310(1)
16.10.2 Methods for Comparisons of Two or More Tests
311(2)
16.11 Software Options and Model Fitting Issues
313(2)
16.12 Interpretation and Reporting
315(2)
16.13 Discussion
317(7)
Chapter 17 Systematic Reviews of Prognostic Factor Studies
324(23)
Richard D. Riley
Karel G.M. Moons
Douglas G. Altman
Gary S. Collins
Thomas P.A. Debray
17.1 Defining the Review Question
326(3)
17.1.1 Application to the C-Reactive Protein Review
328(1)
17.2 Searching and Selecting Eligible Studies
329(1)
17.2.1 Application to the C-Reactive Protein Review
330(1)
17.3 Data Extraction
330(3)
17.3.1 Application to the C-Reactive Protein Review
333(1)
17.4 Evaluating Applicability and Quality of Primary Studies
333(1)
17.4.1 Application to the C-Reactive Protein Review
334(1)
17.5 Meta-Analysis
334(3)
17.5.1 Application to the C-Reactive Protein Review
336(1)
17.6 Quantifying and Examining Heterogeneity
337(1)
17.6.1 Application to the C-Reactive Protein Review
338(1)
17.7 Examining Small-Study Effects
338(1)
17.7.1 Application to the C-Reactive Protein Review
339(1)
17.8 Reporting and Interpretation of Results
339(1)
17.8.1 Application to the C-Reactive Protein Review
340(1)
17.9 Meta-Analysis Using Individual Participant Data
340(1)
17.10 Conclusions
341(6)
Chapter 18 Systematic Reviews of Prediction Models
347(30)
Gary S. Collins
Karel G.M. Moons
Thomas P.A. Debray
Douglas G. Altman
Richard D. Riley
18.1 Framing the Review Question
349(3)
18.2 Identifying Relevant Publications
352(1)
18.3 Data Extraction
353(7)
18.4 Assessing Methodological Quality
360(2)
18.5 Meta-Analysis of Clinical Prediction Model Studies
362(1)
18.6 Case Study: Meta-Analysis of EuroSCORE II
363(5)
18.7 Discussion
368(9)
Chapter 19 Systematic Reviews of Epidemiological Studies of Etiology and Prevalence
377(19)
Matthias Egger
Diana Buitrago-Garcia
George Davey Smith
19.1 Why do we Need Systematic Reviews of Epidemiological Studies?
379(1)
19.2 Meta-Analysis of Epidemiological Studies
380(3)
19.2.1 Bias and Confounding in Etiological Epidemiology: Does Smoking Cause Suicide?
380(1)
19.2.2 Plausible but Spurious?
381(1)
19.2.3 Bias in Prevalence Studies: How Common Is Alcohol Consumption among Students?
382(1)
19.2.4 The Fallacy of Bigger Being Better
383(1)
19.3 Preparing the Systematic Review
383(6)
19.3.1 Shaping the Research Question
384(1)
19.3.2 The Protocol
384(1)
19.3.3 Searching for Relevant Studies
385(1)
19.3.4 Assessing Quality, Risk of Bias, and Study Sensitivity
385(2)
19.3.5 Analysis and Interpretation
387(1)
19.3.5.1 Exploring Sources of Heterogeneity
387(2)
19.3.5.2 Statistical Considerations
389(1)
19.4 Triangulation of Evidence
389(1)
19.5 Conclusion
390(6)
Chapter 20 Meta-Analysis in Genetic Association Studies
396(17)
Gibran Hemani
20.1 Study Designs for Detecting Genetic Associations
397(4)
20.1.1 Natural Genetic Variation
397(1)
20.1.2 Testing for Genetic Association Between a Trait and a Causal Variant
397(1)
20.1.3 Linkage Disequilibrium Aids Detection of Causal Variants
398(1)
20.1.4 The Failure of Candidate Gene Studies
399(1)
20.1.5 The Design of Genome-Wide Association Studies
399(2)
20.2 The Role of Meta-Analysis in Genome-Wide Association Studies
401(7)
20.2.1 The Missing Heritability
401(2)
20.2.2 Replication, and the Use of Meta-Analysis to Overcome the Winner's Curse
403(2)
20.2.3 Sources of Heterogeneity
405(1)
20.2.3.1 Ancestral Differences
406(1)
20.2.3.2 Genotyping
406(1)
20.2.3.3 Trait Definitions
407(1)
20.2.4 Random-Effects or Fixed-Effects Models?
407(1)
20.2.5 Novel Approaches for Using Meta-Analysis with Genome-Wide Association Study Summary Data
407(1)
20.2.5.1 Detecting Study Outliers
407(1)
20.2.5.2 Identifying Single-Nucleotide Polymorphisms that Influence Multiple Traits
408(1)
20.3 Future Prospects
408(5)
PART IV Cochrane and Guideline Development
413(36)
Chapter 21 Cochrane: Trusted Evidence. Informed Decisions. Better Health
415(9)
Gerd Antes
David Tovey
Nancy Owens
21.1 Background and History
416(2)
21.2 Cochrane Groups
418(2)
21.3 Cochrane's Product
420(1)
21.4 Cochrane in the Twenty-First Century
420(1)
21.5 Cochrane in Transition: Challenges and Opportunities
421(3)
Chapter 22 Using Systematic Reviews in Guideline Development: The GRADE Approach
424(25)
Holger J. Schunemann
22.1 Introduction
424(2)
22.1.1 The Role of Systematic Reviews in Guidelines
424(2)
22.1.2 GRADE'S Role in the Systematic Review Process and Guideline Development
426(1)
22.1.3 Who Performs the Assessment of the Certainty in the Evidence?
426(1)
22.2 The Certainty in The Evidence, Quality of the Evidence, or Strength of the Evidence
426(15)
22.2.1 Evidence on the Effects of Interventions
428(1)
22.2.2 Certainty in the Evidence from Randomized Controlled Trials and Nonrandomized Studies can be Lowered in Five Domains
428(1)
22.2.2.1 Risk of Bias
429(1)
22.2.2.2 Inconsistency
429(2)
22.2.2.3 Indirectness
431(1)
22.2.2.4 Imprecision
431(2)
22.2.2.5 Publication Bias
433(1)
22.2.3 Three Factors Can Increase the Certainty in the Evidence of Nonrandomized Studies
433(1)
22.2.3.1 Dose-Response Gradient
433(1)
22.2.3.2 Direction of Plausible Residual Confounding and Bias
434(1)
22.2.3.3 Magnitude of the Effect
434(1)
22.2.4 Certainty in the Evidence by Outcome
434(3)
22.2.5 GRADE Evidence Profiles and Summary of Findings Tables
437(1)
22.2.6 How is the Overall Certainty in the Evidence for a Decision or Recommendation Determined?
437(1)
22.2.7 Assessing the Certainty in a Body of Evidence About Tests
437(4)
22.2.8 Prognosis, Resource Use, and Values and Preferences
441(1)
22.3 Developing Recommendations and Making Decisions
441(3)
22.3.1 The Strength of the Recommendation
441(2)
22.3.2 Research Gaps
443(1)
22.3.3 GRADEpro Software
444(1)
22.4 Outlook
444(5)
PART V Outlook
449(32)
Chapter 23 Innovations in Systematic Review Production
451(12)
Julian Elliott
Tari Turner
23.1 Workflow Platforms
452(1)
23.2 Semi-Automation
452(3)
23.2.1 Study Identification
453(2)
23.2.2 Data Extract/on
455(1)
23.3 Crowdsourcing
455(1)
23.4 Data Structures
455(1)
23.5 Evidence Use
456(1)
23.6 Living Systematic Reviews
456(3)
23.7 Diverse Data
459(1)
23.8 Data Analytics
459(1)
23.9 Conclusions
459(4)
Chapter 24 Future for Systematic Reviews and Meta-Analysis
463(18)
Shah Ebrahim
Mark D. Huffman
24.1 The Demand for Systematic Reviews
463(1)
24.2 Increasing Demand is Good
464(1)
24.3 The Supply Side of Systematic Reviews
465(1)
24.4 New Frontiers for Systematic Reviews
465(2)
24.4.1 When will Basic Medical Sciences Embrace Systematic Reviews
465(1)
24.4.2 Genetics and Novel Systematic Review Methods
466(1)
24.4.3 Wasted Resources, Duplication of Effort
467(1)
24.5 Is the Current World of Systematic Reviews Sustainable?
467(2)
24.5.1 Cochrane
468(1)
24.5.2 Clinical Trial Collaborations
468(1)
24.5.3 Independent Systematic Review Groups
469(1)
24.5.4 Commercial Agencies
469(1)
24.6 Methods for Improving the Process of Creating and Updating Systematic Reviews
469(3)
24.6.1 Informatics
470(1)
24.6.2 Open Data
470(2)
24.6.3 Leveraging the Power of the Crowd
472(1)
24.7 Multiple Interventions and Network Meta-Analysis
472(1)
24.8 Improving Trial Registration, Reporting and Detecting Fraud
473(2)
24.9 Prioritization of Reviews and Updates
475(1)
24.10 Conclusion
475(6)
PART VI Software
481(68)
Chapter 25 Meta-Analysis in Stata
483(27)
David J. Fisher
Marcel Zwahlen
Matthias Egger
Julian P.T. Higgins
25.1 Getting Started
483(3)
25.2 Commands to Perform a Standard Meta-Analysis
486(13)
25.2.1 Example 1 -- Intravenous Streptokinase in Myocardial Infarction
486(1)
25.2.2 The Metan Command
487(3)
25.2.3 Example 2 -- Intravenous Magnesium in Acute Myocardial Infarction
490(3)
25.2.4 Dealing with Zero Cells
493(1)
25.2.5 Heterogeneity Variance and Random Effects
494(2)
25.2.6 The Meta Command Suite in Stata 16
496(3)
25.3 Cumulative and Influence Meta-Analysis
499(3)
25.3.1 Cumulative Meta-Analysis
499(1)
25.3.2 Examining the Influence of Individual Studies
500(2)
25.4 Funnel Plots and Tests for Funnel Plot Asymmetry
502(2)
25.5 Meta-Regression
504(3)
25.5.1 Example 3: Trials of BCG Vaccine Against Tuberculosis
504(3)
25.6 Multivariate and Network Meta-Analysis
507(3)
25.6.1 Multivariate Meta-Analysis
507(1)
25.6.2 Network Meta-Analysis
507(3)
Chapter 26 Meta-Analysis in R
510(25)
Guido Schwarzer
26.1 Getting Started
510(1)
26.2 Installing R Packages for Meta-Analysis
511(1)
26.3 Loading Meta-Analysis Packages
512(1)
26.4 Getting Help
512(3)
26.5 Aspirin in Preventing Death after Myocardial Infarction (Example 1)
515(5)
26.5.1 Meta-Analysis for Example 1 Using R Package Meta
515(3)
26.5.2 Meta-Analysis for Example 1 Using R Package Metafor
518(2)
26.6 Beta-Blocker in Preventing Short-Term Mortality After Myocardial Infarction (Example 2)
520(4)
26.6.1 Meta-Analysis for Example 2 Using R Package Meta
521(1)
26.6.2 Meta-Analysis for Example 2 Using R Package Metafor
522(2)
26.7 Meta-Regression -- Influence of Distance from the Equator on Tuberculosis Vaccine Effectiveness
524(2)
26.8 Evaluation of Bias in Meta-Analysis -- Tests for Small-Study Effects and Trim-and-Fill Method
526(3)
26.9 Other Statistical Methods for Meta-Analysis in R Packages Meta and Metafor
529(2)
26.9.1 Handling of Zero Events in Meta-Analysis of Binary Outcomes
529(1)
26.9.2 Advanced Methods for Meta-Analysis of Binary Outcomes
530(1)
26.9.3 Meta-Analysis for Outcomes Other than Binary Outcomes
530(1)
26.9.4 Estimation of the Between-Study Variance
531(1)
26.9.5 Hartung-Knapp Method -- Alternative Method for Meta-Analysis
531(1)
26.9.6 Prediction Interval
531(1)
26.10 Overview of Other R Packages for Meta-Analysis
531(4)
26.10.1 Bias in Meta-Analysis
531(1)
26.10.2 Network Meta-Analysis
532(1)
26.10.3 Multivariate and Diagnostic Test Accuracy Meta-Analysis
532(3)
Chapter 27 Comprehensive Meta-Analysis Software
535(14)
Michael Borenstein
27.1 Motivating Example
535(1)
27.2 Data Entry
536(1)
27.3 Basic Analysis
536(3)
27.3.1 What is the Average Effect Size?
537(1)
27.3.2 How Much Does the Effect Size Vary?
537(2)
27.4 High-Resolution Plot
539(1)
27.5 Subgroup Analysis
539(3)
27.6 Meta-Regression
542(2)
27.7 Publication Bias
544(2)
27.8 Additional Features in Comprehensive Meta-Analysis
546(1)
27.9 Teaching Elements
546(1)
27.10 Documentation
547(1)
27.11 Availability
547(2)
Index 549
Matthias Egger is Professor of Epidemiology and Public Health at the Institute for Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland and the Centre for Infectious Diseases Epidemiology and Research, University of Cape Town, South Africa.

Julian P.T. Higgins is Professor of Evidence Synthesis at Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

George Davey Smith is Professor of Clinical Epidemiology and Director of the MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
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