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Systematic Reviews in Health Research: Meta-Analysis in Context, Third Edition |
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Comment from the Stata technical groupMeta-analysis is a statistical tool to systematically present new research results in the proper context, given all previous related work. This text is a collection of articles that serve to educate the reader on all the relevant terminology and to bring the reader up to speed on the continuously growing methodology in meta-analysis. By using methods of meta-analysis, researchers may decrease bias and increase the precision of their treatment effects, thus reducing the probability of type I and type II errors and, in the process, making the acceptance of new treatments more timely. The third edition has 12 new chapters covering topics like missing data, network meta-analysis, and dose—response meta-analysis.The remaining chapters from the second edition have been rewritten to reflect recent updates in the literature. The book begins with a chapter highlighting the history and strengths and limitations of systematic reviews and meta-analysis. It is then followed by a section (six chapters) on principles and procedures in systematic reviews, a section (seven chapters) on meta-analysis with a strong emphasis on methodology, and a section (six chapters) on conducting systematic reviews for specific study designs. The last three sections of the book deal with Cochrane and the GRADE approach (two chapters), innovations in and the future for systematic reviews (two chapters), and computer software for meta-analysis (three chapters), with one chapter dedicated to the community-contributed Stata commands and the official meta suite introduced in Stata 16. Stata datasets and additional materials used in the text are available from the book’s website: www.systematic-reviews3.org. |
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Table of contentsView table of contents >> Preface
Tribute
List of Contributors
About the Companion Website
1 Systematic Reviews in Health Research: An Introduction
Matthias Egger, Julian P.T. Higgins, George Davey Smith
1.1 Systematic Review, Meta-Analysis, or Evidence Synthesis?
1.2 The Scope of Meta-Analysis 1.3 Historical Notes 1.4 Why do we Need Systematic Reviews? The Situation in the 1980s 1.5 Traditional Reviews 1.6 Limitations of a Single Study 1.7 A More Transparent and Thorough Appraisal 1.8 The Epidemiology of Results 1.9 What was the Evidence in 1981? 1.10 An Exercise in Mega-Silliness? 1.11 Conclusions Part I: Principles and Prodcedures
2 Principles of Systematic Reviewing
Julian P.T. Higgins, George Davey Smith, Douglas G. Altman, and Matthias Egger
2.1 Developing a Review Protocol
2.2 Presenting, Combining, and Interpreting Results 2.3 Interpreting Findings 2.4 Conclusions 3 Identifying Randomized Controlled Trials
Julie Glanville and Carol Lefebvre
3.1 Searching CENTRAL to Identify Randomized Controlled Trials
3.2 Sources to Search in Addition to CENTRAL 3.3 Searching for Studies Other than Randomized Controlled Trials 3.4 Building Search Strategies 3.5 Conclusion 4 Assessing the Risk of Bias in Randomized Trials
Matthew J. Page, Douglas G. Altman, and Matthias Egger
4.1 Risk of Bias and Quality
4.2 The Evidence Base for Risk of Bias 4.3 Sources of Bias in Randomized Trials 4.4 Approaches to Assessing Risk of Bias in Randomized Trials 4.5 Incorporating Risk of Bias in Meta-Analysis 4.6 Conclusions 5 Investigating and Dealing with Publication Bias and Other Reporting Biases
Matthew J. Page, Jonathan A.C. Sterne, Julian P.T. Higgins, and Matthias Egger
5.1 The Evidence Base for Reporting Biases in Health Research
5.2 Approaches to Minimize Risk of Bias Due to Missing Results 5.3 Approaches to Assess Risk of Bias Due to Missing Results 5.4 Conclusions 6 Managing People and Data
Eliane Rohner, Julia Bohlius, Bruno R. da Costa, and Sven Trelle
6.1 The Team
6.2 The Data 6.3 Outlook:Automation and Data Sharing 7 Reporting and Appraisal of Systematic Reviews
Larissa Shamseer, Beverley Shea, Brian Hutton and David Moher
7.1 Consequences of Poor Reporting
7.2 Reporting Systematic Review Protocols 7.3 Reporting Systematic Reviews 7.4 Reporting Systematic Reviews Without Meta-Analyses 7.5 Other guidance for Reporting Systematic Reviews 7.6 Reporting Other Types of Systematic Reviews 7.7 Optimizing Reporting in Practice 7.8 Appraisal of Systematic Reviews 7.9 Conclusions Part II: Meta Analysis
8 Effect Measures
Julian P.T. Higgins, Jonathan J. Deeks, and Douglas G. Altman
8.1 Individual Study Estimates of Intervention Effect: Binary Outcomes
8.2 Individual Study Estimates of Intervention Effect: Continuous Outcomes 8.3 Individual Study Estimates of Intervention Effect: Time-to-Event Outcomes 8.4 Individual Study Estimates of Intervention Effect: Rates 8.5 Individual Study Estimates of Intervention Effect: Ordinal Outcomes 8.6 Criteria for Selection of a Summary Statistic 8.7 Case Studies 8.8 Discussion 9 Combining Results Using Meta-Analysis
Jonathan J. Deeks, Richard D. Riley, and Julian P.T. Higgins
9.1 Meta-Analysis
9.2 Formulae for Deriving a Summary Estimate of the Intervention Effect in a New Trial by Combining Trial Results (Meta-Analysis) 9.3 Confidence Interval for Overall Effect 9.4 Test Statistic for Overall Effect 9.5 Prediction Interval for the Intervention 9.6 Meta-Analysis with Individual Participation Data 9.7 Additional Analyses 9.8 Some Practical Issues 9.9 Discussion 10 Exploring Heterogeneity
Julian P.T. Higgins and Tianjing Li
10.1 Clinical, Methodological, and Statistical Variability Across Students
10.2 Real and Spurious Heterogeneity 10.3 Subgroup Analysis: Dividing the Evidence into Subsets 10.4 Meta-Regression 10.5 Practical Problems in the Exploration of Heterogeneity 10.6 Closing Remarks 11 Dealing with Missing Outcome Data in Meta-Analysis
Ian R. White and Dimitris Mavridis
11.1 Analysis of a Single Study with Missing Data
11.2 Meta-Analysis with Missing Data 11.3 Method 1: Using Reasons for Missing Data and Simple Assumptions 11.4 Method 2: Quantifying Departures from MAR 11.5 Two Worked Examples 11.6 Recommendations 12 Individual Participant Data Meta-Analysis
Mark C. Simmonds and Lesley A. Stewart
12.1 Advantages and Challenges of Collecting Individual Participant Data
12.2 Performing a Systematic Review Using Individual Participant Data 12.3 Methods for Meta-Analysis with Individual Participant Analysis 12.4 Going Beyond Estimating the Summary Effect 12.5 Individual Participant Data Analysis of Observational Studies 12.6 Combining Individual Participant Data with Published Data 12.7 Reporting Findings 12.8 Conclusion 13 Network Meta-Analysis
Georgia Salanti and Julian P.T. Higgins
13.1 Indirect Comparison and Transitivity
13.2 Indirect and Direct Evidence 13.3 Network Plots of Interventions 13.4 Systematic Reviews Underlying Network Meta-Analysis 13.5 Synthesis of Data 13.6 Intransitvity and Inconsistency 13.7 Ranking Interventions 13.8 Conclusions 14 Dose—Response Meta Analysis
Nicola Orsini, Susanna C. Larsson, and Georgia Salanti
14.1 Example: Coffee Consumption and Mortality Risk
14.2 Estimating Dose—Response Association 14.3 A Linear Trend for a Single Study 14.4 A Quadratic Trend for a Single Study 14.5 A Restricted Cubic Spline Model for a Single Study 14.6 Synthesizing Dose—Response Association Across Studies 14.7 Testing Departure from a Linear Dose—Response Relationship 14.8 Extensions, Limitations,and Developments 14.9 Conclusions Part III: Specific Study Designs
15 Systematic Reviews of Nonrandomized Studies of Interventions
Jelena Savović, Penny F. Whiting, and Olaf M. Dekkers
15.1 The Importance of Nonrandomized Studies in the Evaluation of Interventions
15.2 Defining the Research Question and Eligibility Criteria for the Review 15.3 Searching for Nonrandomized Studies of Interventions 15.4 Risk of Bias 15.5 Synthesizing Results 15.6 Conclusions 16 Systematic Reviews of Diagnostic Accuracy
Yemisi Takwoingi and Jonathan J. Deeks
16.1 Rationale for Undertaking Systematic Reviews of Studies of Test Accuracy
16.2 Features of Studies of Test Accuracy 16.3 Summary Measures of Diagnostic Accuracy 16.4 Measures of Diagnostic Accuracy 16.5 Systematic Reviews of Studies of Diagnostic Accuracy 16.6 Meta-Analysis of Studies of Diagnostic Accuracy 16.7 General Principles of Diagnostic Accuracy Meta-Analysis 16.8 Methods for Meta-Analysis of a Single Test 16.9 Quantifying and Investigating Heterogeneity 16.10 Comparisons of the Accuracy of Two or More Tests 16.11 Software Options and Model Fitting Issues 16.12 Interpretation and Reporting 16.13 Discussion 17 Systematic Reviews of Prognostic Factor Studies
Richard D. Riley, Karel G.M. Moons, Douglas G. Altman, Gary S. Collins, and Thomas P.A. Debray
17.1 Defining the Review Question
17.2 Searching and Selecting Eligible Studies 17.3 Data Extraction 17.4 Evaluating Applicability and Quality of Primary Studies 17.5 Meta-Analysis 17.6 Quantifying and Examining Heterogeneity 17.7 Examining Small-Study Effects 17.8 Reporting and Interpretation of Results 17.9 Meta-Analysis Using Individual Participant Data 17.10 Conclusions 18 Systematic Reviews of Prediction Models
Gary S. Collins, Karel G.M Moons, Thomas P.A. Debray, Douglas G. Altman, and Richard D. Riley
18.1 Framing the Review Question
18.2 Identifying Relevant Publications 18.3 Data Extraction 18.4 Assessing Methodological Quality 18.5 Meta-Analysis of Clincal Prediction Model Studies 18.6 Case Study: Meta-Analysis of EuroSCORE II 18.7 Discussion 19 Systematic Reviews of Epidemiological Studies of Etiology and Prevalence
Matthias Egger, Diana Buitrago-Garcia, and George Davey Smith
19.1 Why do we Need Systematic Reviews of Epidemiological Studies?
19.2 Meta-Analysis of Epidemiological Studies 19.3 Preparing the Systematic Review 19.4 Triangulation of Evidence 19.5 Conclusion 20 Meta-Analysis in Genetic Association Studies
Gibran Hemani
20.1 Study Designs for Detecting Genetic Associations
20.2 The Role of Meta-Analysis in Genome-Wide Association Studies 20.3 Future Prospects Part IV: Cochrane and Guideline Development
21 Cochrane: Trusted Evidence. Informed Decisions. Better Health
Gerd Antes, David Tovey, and Nancy Owens
21.1 Background and History
21.2 Cochrane Groups 21.3 Cochrane's Product 21.4 Cochrane in the Twenty-First Century 21.5 Cochrane in Transition: Challenges and Opportunities 22 Using Systematic Reviews in Guideline Development: The GRADE Approach
Holger J. Schünemann
22.1 Introduction
22.2 The Certainy in The Evidence, Quality of the Evidence, or Strength of the Evidence 22.3 Developing Recommendations and Making Decisions 22.4 Outlook Part V: Outlook
23 Innovations in Systematic Review Production
Julian Elliott and Tari Turner
23.1 Workflow Platforms
23.2 Semi-Automation 23.3 Crowdsourcing 23.4 Data Structures 23.5 Evidence Use 23.6 Living Systematic Reviews 23.7 Diverse Data 23.8 Data Analytics 23.9 Conclusions 24 Future for Systematic Reviews and Meta-Analysis
Shah Ebrahim and Mark D. Huffman
24.1 The Demand for Systematic Reviews
24.2 Increasing Demand is Good 24.3 The Suplpy Side of Systematic Review 24.4 New Frontiers for Systematic Reviews 24.5 Is the Current World of Systematic Reviews Sustainable? 24.6 Methods for Improving the Process of Creating and Updating Systematic Reviews 24.7 Multiple Interventions and Network Meta-Analysis 24.8 Improving Trial Registration, Reporting and Detecting Fraud 24.9 Prioritization of Reviews and Updates 24.10 Conclusion Part VI: Software
25 Meta-Analysis in Stata
David J.Fisher, Marcel Zwahlen, Matthis Egger, and Julian P.T. Higgins
25.1 Getting Started
25.2 Commands to Perform a Standard Meta-Analysis 25.3 Cumulative and Influence Meta-Analysis 25.4 Funnel Plots and Tests for Funnel Plot Asymmetry 25.5 Meta-Regresson 25.6 Multivariate and Network Meta-Analysis 26 Meta-Analysis in R
Guido Schwarzer
26.1 Getting Started
26.2 Installing R Packages for Meta-Analysis 26.3 Loading Meta-Analysis Packages 26.4 Getting Help 26.5 Aspirin in Preventing Death after Myocardial Infarction (Example 1) 26.6 Beta-Blocker in Preventing Short-Term Mortality After Myocardial Infarction (Example 2) 26.7 Meta-Regression - Influence of Distance from the Equator on Tuberculosis Vaccine Effectiveness 26.8 Evaluation of Bias in Meta-Analysis - Tests for Small-Study Effects and Trim-and-Fill Method 26.9 Other Statistical Methods for Meta-Analysis in R Packages Meta and Metaphor 26.10 Overview of Other R Packages for Meta-Analysis 27 Comprehensive Meta-Analysis Software
Michael Borenstein
27.1 Motivating Example
27.2 Data Entry 27.3 Basic Analysis 27.4 High-Resolution Plot 27.5 Subgroup Analysis 27.6 Meta-Regression 27.7 Publication Bias 27.8 Additional Features in Comprehensive Meta-Analysis 27.9 Teaching Elements 27.10 Documentation 27.11 Availability Index
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