Systematic Reviews in Health Research E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 1141

Veröffentlichungsjahr: 2022

Beschreibung

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 reviews—when conducted properly—provide 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.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

Preface

Tribute

List of Contributors

About the Companion Website

CHAPTER 1: Systematic Reviews in Health Research

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

ACKNOWLEDGMENTS

REFERENCES

PART I: PRINCIPLES AND PROCEDURES

CHAPTER 2: Principles of Systematic Reviewing

2.1 DEVELOPING A REVIEW PROTOCOL

2.2 PRESENTING, COMBINING, AND INTERPRETING RESULTS

2.3 INTERPRETING FINDINGS

2.4 CONCLUSIONS

REFERENCES

CHAPTER 3: Identifying Randomized Controlled Trials

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 CONCLUSIONS

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 4: Assessing the Risk of Bias in Randomized Trials

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

REFERENCES

CHAPTER 5: Investigating and Dealing with Publication Bias and Other Reporting Biases

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

REFERENCES

CHAPTER 6: Managing People and Data

6.1 THE TEAM

6.2 THE DATA

6.3 OUTLOOK: AUTOMATION AND DATA SHARING

REFERENCES

CHAPTER 7: Reporting and Appraisal of Systematic Reviews

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

REFERENCES

PART II: META‐ANALYSIS

CHAPTER 8: Effect Measures

8.1 INDIVIDUAL STUDY ESTIMATES OF INTERVENTION EFFECT: BINARY OUTCOMES

8.2 INDIVIDUAL STUDY ESTIMATES OF INTERVENTION EFFECT: CONTINUOUS OUTCOMES

8.6 CRITERIA FOR SELECTION OF A SUMMARY STATISTIC

8.7 CASE STUDIES

8.8 DISCUSSION

REFERENCES

CHAPTER 9: Combining Results Using Meta‐Analysis

9.1 META‐ANALYSIS

9.2 FORMULAE FOR DERIVING A SUMMARY ESTIMATE OF THE INTERVENTION EFFECT 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 EFFECT IN A NEW TRIAL

9.6 META‐ANALYSIS WITH INDIVIDUAL PARTICIPANT DATA

9.7 ADDITIONAL ANALYSES

9.8 SOME PRACTICAL ISSUES

9.9 DISCUSSION

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 10: Exploring Heterogeneity

10.1 CLINICAL, METHODOLOGICAL, AND STATISTICAL VARIABILITY ACROSS STUDIES

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

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 11: Dealing with Missing Outcome Data in Meta‐Analysis

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

REFERENCES

CHAPTER 12: Individual Participant Data Meta‐Analysis

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 DATA

12.4 GOING BEYOND ESTIMATING THE SUMMARY EFFECT

12.5 INDIVIDUAL PARTICIPANT DATA META‐ANALYSIS OF OBSERVATIONAL STUDIES

12.6 COMBINING INDIVIDUAL PARTICIPANT DATA WITH PUBLISHED DATA

12.7 REPORTING FINDINGS

12.8 CONCLUSION

REFERENCES

CHAPTER 13: Network Meta‐Analysis

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 INTRANSITIVITY AND INCONSISTENCY

13.7 RANKING INTERVENTIONS

13.8 CONCLUSIONS

REFERENCES

CHAPTER 14: Dose–Response Meta‐Analysis

14.1 EXAMPLE: COFFEE CONSUMPTION AND MORTALITY RISK

14.2 ESTIMATING DOSE–RESPONSE ASSOCIATION WITHIN A STUDY

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

REFERENCES

PART III: SPECIFIC STUDY DESIGNS

CHAPTER 15: Systematic Reviews of Nonrandomized Studies of Interventions

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

REFERENCES

CHAPTER 16: Systematic Reviews of Diagnostic Accuracy

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

REFERENCES

CHAPTER 17: Systematic Reviews of Prognostic Factor Studies

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

REFERENCES

CHAPTER 18: Systematic Reviews of Prediction Models

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 CLINICAL PREDICTION MODEL STUDIES

18.6 CASE STUDY: META‐ANALYSIS OF EUROSCORE II

18.7 DISCUSSION

REFERENCES

CHAPTER 19: Systematic Reviews of Epidemiological Studies of Etiology and Prevalence

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

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 20: Meta‐Analysis in Genetic Association Studies

20.1 STUDY DESIGNS FOR DETECTING GENETIC ASSOCIATIONS

20.2 THE ROLE OF META‐ANALYSIS IN GENOME‐WIDE ASSOCIATION STUDIES

20.3 FUTURE PROSPECTS

REFERENCES

PART IV: COCHRANE AND GUIDELINE DEVELOPMENT

CHAPTER 21: Cochrane: Trusted Evidence. Informed Decisions. Better Health

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

ACKNOWLEDGMENTS

REFERENCES

CHAPTER 22: Using Systematic Reviews in Guideline Development The GRADE Approach

22.1 INTRODUCTION

22.2 THE CERTAINTY IN THE EVIDENCE, QUALITY OF THE EVIDENCE, OR STRENGTH OF THE EVIDENCE

22.3 DEVELOPING RECOMMENDATIONS AND MAKING DECISIONS

22.4 OUTLOOK

ACKNOWLEDGMENTS

REFERENCES

PART V: OUTLOOK

CHAPTER 23: Innovations in Systematic Review Production

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

REFERENCES

CHAPTER 24: Future for Systematic Reviews and Meta‐Analysis

24.1 THE DEMAND FOR SYSTEMATIC REVIEWS

24.2 INCREASING DEMAND IS GOOD

24.3 THE SUPPLY SIDE OF SYSTEMATIC REVIEWS

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

ACKNOWLEDGMENTS

REFERENCES

PART VI: SOFTWARE

CHAPTER 25: Meta‐Analysis in Stata

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‐REGRESSION

25.6 MULTIVARIATE AND NETWORK META‐ANALYSIS

REFERENCES

CHAPTER 26: Meta‐Analysis in R

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 METAFOR

26.10 OVERVIEW OF OTHER R PACKAGES FOR META‐ANALYSIS

REFERENCES

CHAPTER 27: Comprehensive Meta‐Analysis Software

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

ACKNOWLEDGMENTS

REFERENCES

Index

End User License Agreement

List of Tables

Chapter 1

TABLE 1.1 Conclusions from four randomized controlled trials of beta‐blocke...

Chapter 2

TABLE 2.1 Characteristics of long‐term trials comparing beta‐blockers with ...

TABLE 2.2 Meta‐analysis of trials of BCG vaccination to prevent tuberculosi...

TABLE 2.3 Beta‐blockade in secondary prevention after myocardial infarction...

Chapter 3

TABLE 3.1 Key resources for identifying systematic reviews and reports of t...

TABLE 3.2 Search syntax.

Chapter 4

TABLE 4.1 Eligible sources of bias in randomized trials.

TABLE 4.2 Example of a completed Cochrane risk of bias table for a trial co...

Chapter 5

TABLE 5.1 Possible sources of asymmetry in funnel plots.

Chapter 6

TABLE 6.1 Example of a data extraction field for risk of bias assessment.

Chapter 7

TABLE 7.1 PRISMA 2020 Checklist.

TABLE 7.2 Extensions of the PRISMA statement.

TABLE 7.3 Reliability and validity of ROBIS and AMSTAR/AMSTAR 2.

Chapter 8

TABLE 8.1 Types of data arising from individual participants in a randomize...

TABLE 8.2 Summary information when outcome is binary.

TABLE 8.3 Summary information when outcome is continuous.

TABLE 8.4 Summary information for computing rates.

TABLE 8.5 Results of four hypothetical trials with varying control group ri...

TABLE 8.6 Distribution of

I

2

statistics for different outcome measures....

TABLE 8.7 Alternative analyses of eradication trials for non‐ulcer dyspepsi...

TABLE 8.8 Alternative analyses of influenza vaccination trials.

Chapter 9

TABLE 9.1 Summary information when outcome is binary.

TABLE 9.2 Rates of use of epidural anesthesia in trials of caregiver suppor...

TABLE 9.3 Results of meta‐analyses of epidural rates from trials of caregiv...

TABLE 9.4 Impact of salt‐lowering diets on systolic and diastolic blood pre...

Chapter 10

TABLE 10.1 Meta‐regression results using the number of sessions to explain ...

Chapter 11

TABLE 11.1 Methods for handling missing outcome data in clinical trials.

TABLE 11.2 Haloperidol meta‐analysis: main results and reasons for missing ...

TABLE 11.3 Mirtazapine meta‐analysis: mean change in depression scores, sta...

Chapter 12

TABLE 12.1 Results of one‐ and two‐stage analyses of PARIS review.

TABLE 12.2 Results of one‐ and two‐stage analyses of the effect of covariat...

Chapter 13

TABLE 13.1 Special considerations in a systematic review and network meta‐a...

TABLE 13.2 Results from network meta‐analysis and pairwise meta‐analysis fo...

Chapter 14

TABLE 14.1 Summarized data for the study by Klatsky et al. [23] including t...

TABLE 14.2 Mean

risk ratio

(

RR

) for all‐cause mortality and 95%

confidence

...

Chapter 15

TABLE 15.1 Risk of bias domains assessed in the ROBINS‐I tool.

Chapter 16

TABLE 16.1 Cross‐classification of index test and reference standard result...

TABLE 16.2 Effect of age group on sensitivity and specificity of Xpert MTB/...

TABLE 16.3 Summary estimates from direct and indirect comparisons of

comput

...

TABLE 16.4 Comparison of the accuracy of bipolar spectrum diagnostic scale ...

Chapter 18

TABLE 18.1 The PICOTS system, as presented in the CHARMS guidance and check...

TABLE 18.2 Formulas for estimating the logit

c

statistic and its variance f...

TABLE 18.3 Formulas for estimating the ln(O : E) ratio from other informati...

TABLE 18.4 Summarized results of the 22 validation studies of the additive ...

Chapter 19

TABLE 19.1 Characteristics of 100 articles sampled at random from articles ...

Chapter 22

TABLE 22.1 GRADE categories of certainty in the evidence.

TABLE 22.2 Guidance for the risk of bias domain in a GRADE assessment: goin...

TABLE 22.3 Judgments about indirectness by outcome.

TABLE 22.4 Domains for describing certainty in the evidence and justifying ...

TABLE 22.5 Example of a GRADE evidence profile.

TABLE 22.6 Example of a GRADE summary of findings table.

TABLE 22.7 Criteria that influence the strength and direction in the GRADE ...

TABLE 22.8 Interpretation of the certainty in a body of evidence according ...

Chapter 23

TABLE 23.1 Estimated time to perform specific tasks required to produce a s...

TABLE 23.2 Approaches to semi‐automation of study identification.

Chapter 25

TABLE 25.1 Summary of useful Stata commands for meta‐analysis

TABLE 25.2 Data from 22 randomized controlled trials of streptokinase in th...

TABLE 25.3 Data from 16 randomized controlled trials of intravenous magnesi...

TABLE 25.4 Data from 11 studies of BCG vaccine to prevent

tuberculosis

(

TB

)...

List of Illustrations

f04

FIGURE T.1 The contents and contributors to the first edition of the book on...

Chapter 1

FIGURE 1.1 Number of publications concerning meta‐analysis, 1987–2020. Resul...

FIGURE 1.2 Title page of the first “textbook” of meta‐analysis, 1861.

FIGURE 1.3 Distinguished statistician Karl Pearson (1857–1936) is seen as th...

FIGURE 1.4 Forest plot showing mortality results from trials of beta‐blocker...

FIGURE 1.5 Cumulative meta‐analysis of controlled trials of beta‐blockers af...

FIGURE 1.6 Cumulative meta‐analysis of randomized controlled trials of intra...

Chapter 2

FIGURE 2.1 Forest plot showing total mortality from trials of beta‐blockers ...

FIGURE 2.2 Forest plot of trials of BCG vaccine to prevent tuberculosis. Tri...

FIGURE 2.3 Sensitivity analyses examining the robustness of the effect on to...

Chapter 3

FIGURE 3.1 Number of randomized and other controlled trials in PubMed (Rando...

FIGURE 3.2 Sources contributing reports of randomized controlled trials to C...

Chapter 4

FIGURE 4.1 Example of the use of meta‐epidemiology to investigate bias due t...

FIGURE 4.2 Sources of bias in randomized trials.

FIGURE 4.3 Results of syntheses of meta‐epidemiological studies relating met...

FIGURE 4.4 Example presentation of risk of bias assessments (using the 2011 ...

FIGURE 4.5 Domains of bias in the initial and revised Cochrane risk of bias ...

Chapter 5

FIGURE 5.1 Random‐effects meta‐analysis of meta‐analyses investigating the a...

FIGURE 5.2 Random‐effects meta‐analysis of studies investigating the associa...

FIGURE 5.3 Hypothetical funnel plots: (a) symmetric plot in the absence of r...

FIGURE 5.4 Contour‐enhanced funnel plot for meta‐analysis of the effect of

s

...

FIGURE 5.5 Contour‐enhanced funnel plot for meta‐analysis of the effect of h...

Chapter 6

FIGURE 6.1 Gantt chart for planning and visualizing the schedule of a system...

FIGURE 6.2 Digitizing a Kaplan–Meier curve using digitizing software.

Chapter 7

FIGURE 7.1 PRISMA 2020 flow diagram. Gray boxes should only be completed if ...

Chapter 8

FIGURE 8.1 Risk ratios and odds ratios are similar when the overall risk is ...

FIGURE 8.2 L'Abbé plots demonstrating constant odds ratios, risk differences...

FIGURE 8.3 Comparison of heterogeneity of the same 551 meta‐analyses using r...

FIGURE 8.4 L'Abbé plot of the results of the five trials of

H. pylori

eradic...

FIGURE 8.5 Predictions of treatment benefit at 6–12 months using

H. pylori

e...

FIGURE 8.6 L'Abbé plot of the results of 20 trials of influenza vaccination ...

Chapter 9

FIGURE 9.1 Summary meta‐analysis results for a meta‐analysis of the effect o...

FIGURE 9.2 Forest plots of two distinct hypothetical meta‐analyses that give...

Chapter 10

FIGURE 10.1 Meta‐analysis of trials comparing the effect of caregiver suppor...

FIGURE 10.2 Within‐study subgroup analyses examining the effect of corticost...

FIGURE 10.3 Meta‐regression analysis using latitude to explain variation in ...

FIGURE 10.4 Meta‐regression analysis using the number of sessions to explain...

Chapter 11

FIGURE 11.1 Plausible distribution for the informative missingness odds rati...

FIGURE 11.2 Haloperidol meta‐analysis under four different assumptions about...

FIGURE 11.3 Plausible distribution for the informative missingness differenc...

FIGURE 11.4 Mirtazapine meta‐analysis under two different assumptions about ...

Chapter 12

FIGURE 12.1 Popularity of individual participant data (IPD) meta‐analyses ov...

FIGURE 12.2 Forest plot of the impact of aspirin on pre‐eclampsia in the PAR...

FIGURE 12.3 Results of the two‐stage individual participant data (IPD) meta‐...

FIGURE 12.4 Results of the individual participant data (IPD) meta‐analysis f...

Chapter 13

FIGURE 13.1 Complex evidence network of 26 trials comparing percutaneous cor...

FIGURE 13.2 Network with two closed loops: low‐dose unfractionated heparin (...

FIGURE 13.3 Network of trials of tyrosine‐kinase inhibitors in the treatment...

FIGURE 13.4 Star network of trials of antiepileptic drugs for the treatment ...

FIGURE 13.5 Complex network of trials of 12 new‐generation antidepressants t...

FIGURE 13.6 Rankograms from network of trials of 12 new‐generation antidepre...

FIGURE 13.7 Cumulative ranking for six biologics for rheumatoid arthritis wi...

Chapter 14

FIGURE 14.1 Dose–response association between coffee consumption and all‐cau...

FIGURE 14.2 Summary of the study‐specific (log) linear trends, expressed for...

FIGURE 14.3 Mean dose–response association between coffee consumption and al...

Chapter 15

FIGURE 15.1 Interrupted time series of a pay‐for‐performance scheme. The obs...

FIGURE 15.2 Graphical representation of confounding in nonrandomized studies...

FIGURE 15.3 Forest plot showing relative risk of cardiovascular events for b...

FIGURE 15.4 Forest plot showing risk ratios for all‐cause mortality for a co...

Chapter 16

FIGURE 16.1 Receiver operating characteristic plot for detecting endometrial...

FIGURE 16.2 Summary of review authors’ risk of bias and applicability concer...

FIGURE 16.3 Forest plot of the mood disorder questionnaire for detection of ...

FIGURE 16.4 SROC plot of the mood disorder questionnaire (MDQ) at a common t...

FIGURE 16.5 SROC plot of the

mood disorder questionnaire

(

MDQ

) across differ...

FIGURE 16.6 Accuracy of Xpert MTB/RIF for detecting pulmonary tuberculosis i...

FIGURE 16.7 Comparison of summary points. (a) Indirect comparison. (b) Direc...

FIGURE 16.8 Comparison of summary curves. Panel a shows the indirect compari...

Chapter 17

FIGURE 17.1 Tumor grade as a prognostic factor in breast cancer [2]. Kaplan–...

FIGURE 17.2 Forest plot showing the study‐specific estimates and meta‐analys...

FIGURE 17.3 Evidence of funnel plot asymmetry (small‐study effects) in the C...

Chapter 18

FIGURE 18.1 Example of calibration plot of a prediction model.

FIGURE 18.2 Forest plot of the extracted

c

indices from the 22 validation st...

FIGURE 18.3 Overall calibration of EuroSCORE II, summarized from the 22 vali...

FIGURE 18.4 Association between mean EuroSCORE II and overall O : E. Circle ...

Chapter 19

FIGURE 19.1 Adjusted relative rates of suicide among middle‐aged male smoker...

FIGURE 19.2 “Today's Random Medical News”: Observational studies produce a l...

FIGURE 19.3 Examples of heterogeneity in published observational meta‐analys...

FIGURE 19.4 Meta‐regression of genital chlamydia prevalence estimates in wom...

Chapter 20

FIGURE 20.1 Schematic of how imputation aids in meta‐analysis of genome‐wide...

FIGURE 20.2 Schematic of known genetic associations. The NHGRI‐EBI

genome‐wi

...

FIGURE 20.3 Improvements in

genome‐wide association study

(

GWAS

) resul...

Chapter 21

FIGURE 21.1 The Cochrane logo illustrates a systematic review of seven rando...

Chapter 22

FIGURE 22.1 The guideline development process: summarizing the evidence and ...

FIGURE 22.2 Integration of the GRADE approach into guideline development for...

FIGURE 22.3 Obtaining a final rating for the certainty in the evidence. Cert...

Chapter 23

FIGURE 23.1 Current and emerging health knowledge ecosystems. The current he...

FIGURE 23.2 Cochrane’s evidence pipeline. As an alternative to searching bib...

Chapter 24

FIGURE 24.1 Relationship between discrepancies per trial and effect size (me...

Chapter 25

FIGURE 25.1 Forest plot of the data in Table 25.2 using

metan

with Mantel–...

FIGURE 25.2 Forest plot of the data in Table 25.3 using

metan

with Mantel–...

FIGURE 25.3 Forest plot of the data in Table 25.3 using

metan

with inverse...

FIGURE 25.4 Forest plot of the data in Table 25.2 using

meta

with Mantel–H...

FIGURE 25.5 Forest plot of the data in Table 25.3 using

metan

with cumulat...

FIGURE 25.6 Funnel plot of the data in Table 25.3 using risk ratios.

FIGURE 25.7 Forest plot of the data in Table 25.4 using

metan

with Mantel–...

Chapter 26

FIGURE 26.1 Help page with brief overview of R package

meta

.

FIGURE 26.2 Forest plot for aspirin meta‐analysis [5] using the

forest.meta

...

FIGURE 26.3 Forest plot for aspirin meta‐analysis [5] using the

forest.rma

f...

FIGURE 26.4 Forest plot for beta‐blocker meta‐analysis [6] in Review Manager...

FIGURE 26.5 Bubble plot for meta‐regression of BCG vaccine dataset [7] using...

FIGURE 26.6 Funnel plot for aspirin meta‐analysis [5] using the

funnel.meta

...

Chapter 27

FIGURE 27.1 Data‐entry screen.

FIGURE 27.2 Basic analysis screen.

FIGURE 27.3 Average effect size (left), variation in effect size (right)....

FIGURE 27.4 Plot of true effects and prediction interval.

FIGURE 27.5 High‐resolution forest plot.

FIGURE 27.6 Impact of treatment as a function of subgroup (Major Only vs. Ma...

FIGURE 27.7 Impact of treatment as a function of subgroup (Major Only vs. Ma...

FIGURE 27.8 Results for regression, random effects.

FIGURE 27.9 Regression of log odds ratio on Baseline, with Dose held constan...

FIGURE 27.10 Funnel plot and adjustment based on trim and fill.

Guide

Cover Page

Title Page

Copyright Page

Preface

Tribute

List of Contributors

About the Companion Website

Table of Contents

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Systematic Reviews in Health Research

Meta‐Analysis in Context

THIRD EDITION

Edited by

Matthias Egger

Professor of Epidemiology and Public Health

Institute for Social and Preventive Medicine (ISPM)

University of Bern, Bern, Switzerland

&

Centre for Infectious Diseases Epidemiology and Research

University of Cape Town, South Africa

Julian P.T. Higgins

Professor of Evidence Synthesis

Population Health Sciences

Bristol Medical School

University of Bristol, Bristol, UK

George Davey Smith

Professor of Clinical Epidemiology and Director of the MRC Integrative Epidemiology Unit

University of Bristol, Bristol, UK

This third edition first published 2022© 2022 John Wiley & Sons Ltd

Edition HistoryBMJ Publishing Group (Systematic Reviews in Health Care 2e, 2001); (Systematic Reviews in Health Care 1e, 1995)

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

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Library of Congress Cataloging‐in‐Publication Data

Names: Egger, Matthias, editor. | Higgins, Julian P. T., editor. | Davey Smith, George, editor.Title: Systematic reviews in health research : meta‐analysis in context / edited by Matthias Egger, Julian P. T. Higgins, George Davey Smith.Other titles: Systematic reviews in health care (Egger)Description: Third edition. | Hoboken, NJ : John Wiley & Sons, Inc., 2022. | Prev. ed.: Systematic reviews in health care / edited by Matthias Egger, George Davey Smith and Douglas G. Altman. 2nd ed. 2001.Identifiers: LCCN 2021049316 (print) | LCCN 2021049317 (ebook) | ISBN 9781405160506 (hardback) | ISBN 9781119099376 (adobe pdf) | ISBN 9781119099383 (epub)Subjects: MESH: Systematic Reviews as Topic | Meta‐Analysis as Topic | Research Design | Evidence‐Based Medicine | Controlled Clinical Trials as TopicClassification: LCC R853.S94 (print) | LCC R853.S94 (ebook) | NLM W 20.5 | DDC 610/.7/2–dc23/eng/20211029LC record available at https://lccn.loc.gov/2021049316LC ebook record available at https://lccn.loc.gov/2021049317

Cover Design: WileyCover Image: © Mina De La O/Getty Images

Preface

Iain Chalmers and Doug Altman edited the first edition of this book, which was published in 1995 and called simply Systematic Reviews. Their foreword focused on the “poor scientific quality of [traditional] reviews of clinical research” and the “disregard of scientific principles,” which may harm decision‐making and patient outcomes [1]. Systematic reviews allow a more objective appraisal by systematically identifying, scrutinizing, and synthesizing the relevant studies. Today, more than a quarter of a century and two editions later [1, 2], systematic reviews and meta‐analyses have become widely established, with thousands of such studies published every year. Also, their function has broadened, from the scientific synthesis of evidence to inform clinical practice, the main concern in 1995, to becoming part of the methods toolkit to address a wide range of questions, including evaluation of interventions, diagnosis and prognosis, prevalence and burden of disease, and discovery research.

This broader role is reflected in the current third edition. Of the 27 chapters, 12 are new, covering systematic reviews of prediction models, genetic association studies, and prevalence studies. The focus on methods has become stronger too, with new chapters dealing with missing data, network meta‐analysis, and dose–response meta‐analysis. These changes explain the book's new title (Systematic Reviews in Health Research, rather than in Health Care) and revised structure. The first chapter discusses the rationale, history, and strengths and limitations of systematic reviews and meta‐analysis. This introductory chapter is followed by a section on principles and procedures (six chapters), a section on meta‐analysis (seven chapters), and one on systematic reviews and meta‐analysis of specific study designs (six chapters). Two chapters cover Cochrane (formerly the Cochrane Collaboration) and systematic reviews and meta‐analyses in guideline development and the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation). The book ends with an outlook on innovations in and the future of systematic reviews and meta‐analysis (two chapters) and a section on software (three chapters). The 15 chapters that made the journey from the second to the third edition have all been thoroughly updated. Nevertheless, all chapters will age, some more quickly than others. The book's website (www.systematic‐reviews3.org) will be our antidote to premature aging, by offering updates of some chapters (for example, those on software packages) and highlighting references to recent critical articles and instruments, new software, and other developments.

The intended audience will be similar to that for the popular second edition [2]: methodologically inclined clinicians, epidemiologists, health services researchers, and public health specialists interested in conducting high‐quality systematic reviews and meta‐analyses. The book will also be of interest to doctoral and other students in the health sciences. Indeed, we see this book as an excellent resource for teaching and will provide exercises and computer practicals on the companion website.

On the long journey from the second to the third edition, we lost our dear friend, collaborator, and co‐editor, Douglas Graham Altman. Doug sadly died, aged 69, on June 3, 2018. He made essential contributions to this third edition, helping define focus and content and co‐authoring several chapters. On the following pages, we reprint Iain Chalmers' wonderful tribute to Doug's visionary role in developing systematic reviews and clarifying the role of meta‐analysis [3]. We miss you very much, Doug, and dedicate this book to you.

Last but not least, huge thanks to all contributors to this book. They made it possible by patiently and stoically updating their chapters over several years. Many thanks to our project managers and editors in Bern, Carole Dupont, Chris Ritter, and Geraldine Wong, who supported us so well on this long journey. A big thank‐you also to the editors at Wiley, Jennifer Seward, Samras Johnson V, and Ella Elliot, for their brilliant help and their patience.

The first edition of this book was applauded for its “simple and often humorous” discussion and its “intuitively appealing explanations” [4]. We hope you will agree that the third edition continues in this tradition.

Bern and Bristol, April 2022Matthias Egger, Julian P.T. Higgins, George Davey Smith

REFERENCES

1. Chalmers, I. and Altman, D. (1995). Foreword. In:

Systematic Reviews

(ed. I. Chalmers and D. Altman). London: BMJ Publishing Group.

2. Egger, M., Davey Smith, G., Altman, D.G. et al. (ed.) (2001).

Systematic Reviews in Health Care: Meta‐Analysis in Context

, 2e. London: BMJ Books.

3. Chalmers, I. (2020). Doug Altman's prescience in recognising the need to reduce biases before tackling imprecision in systematic reviews.

J. R. Soc. Med.

113: 119–122. https://doi.org/10.1177/0141076820908496.

4. DerSimonian, R. (1997). Book Review: Systematic Review. I. Chalmers and D. G. Altman (eds), BMJ Publishing Group, London, 1995. No. of pages: 117. ISBN 0‐7279‐0904‐5.

Stat. Med.

16: 2930–2930.

Tribute

Professor Doug Altman (1948–2018) co-edited the first and second editions of this book. Here, Sir Iain Chalmers, founder of The Cochrane Collaboration and the James Lind Alliance, reflects on Altman’s seminal contributions to the concept of systematic reviews and the role of meta-analysis.

Doug Altman’s Prescience in Recognizing the Need to Reduce Biases before Tackling Imprecision in Systematic Reviews

Iain Chalmers

RECOGNITION OF SHARED INTERESTS AND THE ORIGINS OF A FRIENDSHIP

I came to know Doug Altman during the 1980s when we were both members of the editorial team at the British Journal of Obstetrics and Gynaecology. I was working at the National Perinatal Epidemiology Unit at that time; Doug was at the Division of Medical Statistics at the Medical Research Council's Clinical Research Centre. Our meeting at the BJOG was the beginning of what became a very close friendship.

Doug and I shared an interest in trying to improve the quality of the manuscripts submitted to the BJOG. We commissioned three papers providing reporting guidelines for those submitting reports of controlled trials, assessments of screening and diagnostic tests, and observational studies – early examples of an interest that would become manifested in Doug's creation of the EQUATOR Network (Enhancing the QUAlity and Transparency Of health Research).

We also discovered that we had both become interested in the scientific quality of reviews of research evidence, and the potential for statistical synthesis of estimates derived from several similar studies. I had used this approach in a review of four randomized comparisons of different ways of monitoring fetuses during labour [1], the results of which prompted a very large further controlled trial that confirmed the results of the meta‐analysis [2].

Doug's interest in the scientific quality of reviews of research evidence had been stimulated by two papers published in the late 1970s by Richard Peto [3, 4]. These led Doug to prepare a seven‐page typescript entitled “Evaluating a series of clinical trials of the same treatment” for presentation at the 1981 meeting of the International Epidemiological Association in Edinburgh [5]. Over the next two years Doug extended the material in the 7‐page typescript to a 40‐page typescript with the same title [6].

DOUG'S PIONEERING CONCEPTUALIZATION OF SYSTEMATIC REVIEWS AND THE ROLE OF META‐ANALYSIS

Doug's 1983 paper is important in the history of systematic reviews because of his prescience of what is important in the science of research synthesis. Unfortunately, it has been hidden from view because it was never formally published. I think Doug first showed me “the almost final version of [his] 1983 paper (complete with handwritten corrections)” at the end of 1986. He said he intended to finalize and submit it for publication, but that did not happen. As he admitted more than two decades later, “I wish I had published my ideas back in 1983” [7]. Since 2011, the typescripts of both papers [5, 6] have been available in the James Lind Library, and the shorter paper, with an accompanying commentary by Doug, is also available in the Cochrane Methods supplement to the Cochrane Database of Systematic Reviews[8].

In both these papers Doug touched on issues that would become more widely recognized as important by the 1990s. In particular, he made clear that techniques of statistical synthesis – “meta‐analysis” – were but one element in a science of research synthesis, and usually not the most important. He made clear that, although statistical synthesis could address those elements of between‐study variability due to random variation, it could not deal with other sources of variability – differences in entry criteria, study populations, the methods used to generate comparison groups, baseline differences between treatment groups, degrees of blindness achieved, and variations in and deviations from treatment protocols. Doug comments at the beginning of a nine‐page section on “Combining the data” in the longer paper that “Since the main purpose of the paper is to discuss the whole issue of whether or not to combine trials rather than to carry out a comparison of the available methods, not all of the possible statistical methods will be described” [6]. Both his papers stressed the likely importance of publication bias and he regretted the lack (then) of hard evidence of the bias and the challenges this posed. He makes the important and too often neglected point:

Although the problem of possible publication bias may appear to be a major restriction on the validity of combining the results from several trials, it is important to realise that any such bias applies to the interpretation of individual studies, although this is always ignored and each study's results taken at face value.

([6], p. 25)

Toward the end of his 1983 paper, Doug presciently identified two desirable developments that would become widely appreciated by the end of the decade. First, the use of individual patient data:

In view of the non‐statistical problems in the combination of results from different trials, the choice of statistical method is unlikely to matter greatly, but methods which make use of the raw data are definitely preferable to the combination of probabilities. The pooled estimate of relative risk should be presented with its confidence interval.

([6], p. 33)

Secondly, there is a paragraph in a section of the paper entitled “Ethical considerations” that anticipates developments in thinking and practice during the 1980s and 1990s, which Doug selected for attention after re‐reading his paper over 30 years after drafting it [8]. Here's the paragraph that had struck him:

[it] is important to consider whether the results of a series of studies of the same treatment should be accumulated on a regular basis in order to monitor the current state of knowledge about those treatments. Further trials might then be dependent on the combined significance of already completed trials but using a stricter level of statistical significance (say P < 0.001) than is usually applied in single trials. Even without such information trials should perhaps not be given ethical committee approval unless the researchers had analysed the results of published trials in the way suggested in order to demonstrate that there was still uncertainty about the efficacy of the treatment, and the range of uncertainty encompassed clinically relevant benefit. Further, power calculations for a new trial could be conditional on the results of published trials.

([6], p. 27)

THE ORIGINS OF SYSTEMATIC REVIEWS IN HEALTH RESEARCH: META‐ANALYSIS IN CONTEXT

Following wider recognition of the need to improve the scientific quality of reviews [9–11], the opening of the Cochrane Centre in Oxford in October 1992 helped to generate interest in the science of research synthesis [12]. I was delighted that Richard Smith, editor of the British Medical Journal, recognized this and proposed an all‐day meeting run jointly by the BMJ and The Cochrane Centre. I was very glad that he accepted that the title of the meeting would refer to systematic reviews, and not to meta‐analysis, as had been proposed originally. The meeting was held at the Royal Institution on 7 July 1993. Eight presentations covered the development of systematic reviews; doubts about them and the challenge of finding relevant studies; rationale and practicalities; and assessing, updating, and disseminating systematic reviews.

Based on the presentations made at the meeting, a series of articles about systematic reviews began in the 3 September 1994 issue of the BMJ. In his “Editor's Choice” column, Richard Smith noted that systematic review was “one of the most valuable tools in assessing new treatments and technologies” [13]. He was even more supportive in his Editor's Choice column a few weeks later:

Systematic reviews provide the highest quality evidence on treatment… The author of a systematic review poses a clear question, gathers all relevant trials (whether published or not), weeds out the scientifically flawed, and then amalgamates the remaining trials to reach a conclusion. Every stage in the process is crucial, and an article in the journal by Kay Dickersin and her colleagues shows how a careful Medline search for randomised controlled trials will not detect all such trials even in the journals indexed in Medline.

[14]

Richard Smith went on to point out that systematic reviews are also important because – by amalgamating data from similar trials – they can increase the statistical power of treatment comparisons [14]. These succinct explanations of the rationale for systematic reviews made by the Editor‐in‐Chief of one of the world's most prominent medical journals were heartening to those of us calling for improvements in the scientific quality of reviews of research.

The BMJ's series of articles on systematic reviews was well received and Richard Smith proposed that I should edit a compilation of the articles as a book. I accepted, on condition that Doug Altman would co‐edit it with me, and I was very glad that both Richard and Doug agreed [15]. The contents and contributors to the book are shown in Figure T.1 and in the James Lind Library at https://www.jameslindlibrary.org/chalmers‐i‐altman‐dg‐1995. The book introduces and illustrates systematic reviews; discusses data collection for them; presents contrary stances on the value of using meta‐analysis to generate overall summary statistics; provides guidelines for assessing the trustworthiness of reviews; describes how systematic reviews are being prepared, updated, and disseminated by the international network of people who together constitute the Cochrane Collaboration; and concludes with a classified bibliography for further reading. The book is dedicated to Thomas C. Chalmers, “in appreciation of his many pioneering contributions to the science of reviewing health research, and in particular, for the first clear demonstration of the dangers of relying on traditional reviews of research to guide clinical practice.”

Doug's and my Preface in the book provided an opportunity to explain why we had used the term “systematic review” rather than the more technical neologism “meta‐analysis”:

Use of the term ‘systematic review’ implies only that a review has been prepared using some kind of systematic approach to minimising biases and random errors, and that the components of the approach will be documented in a materials and methods section. Other terms – particularly ‘meta‐analysis’ – have caused confusion because of the implication that a systematic approach to reviews must entail quantitative synthesis of primary data to yield an overall summary statistic (meta‐analysis). As we hope this book will help to make clear, this is not the case. In addition to those circumstances in which statistical synthesis (meta‐analysis) of results of primary research is not advisable, there will be others in which it is quite simply impossible. It is just as important to take steps to control biases in reviews in these circumstances as it is to do so in circumstances in which meta‐analysis is both indicated and possible.

[15]

Doug reiterated this point in his 2013 commentary on “Twenty years of meta‐analysis and evidence synthesis methods.” He wrote:

FIGURE T.1 The contents and contributors to the first edition of the book on systematic reviews

As time went on we have realized that there are many hidden problems, nuances, extensions, and so on. And there have been big changes in strategy. The biggest impact probably came from the early realization that the statistical analysis is a relatively simple part of a rather complex set of actions which we now label as a systematic review.

[8]

The issue was dealt with nicely in the title chosen for the second edition of the book, namely Systematic Reviews in Health Care: Meta‐Analysis in Context[16]. I am grateful to the editors of the third edition of the book (Egger, Davey Smith, and Higgins) for inviting me to draw attention to the pioneering thinking and unpublished writing about research synthesis by their and my much‐loved, late‐lamented co‐editorial colleague, Doug Altman.

Note

This text was published previously as Chalmers I. Doug Altman's prescience in recognising the need to reduce biases before tackling imprecision in systematic reviews. Journal of the Royal Society of Medicine 2020; 113 : 119–122.

ACKNOWLEDGMENTS

I am grateful to Mike Clarke, George Davey Smith, Anne Eisinga, Julian Higgins, and Richard Smith for comments on an earlier draft of this text.

REFERENCES

1. Chalmers, I. (1979). Randomised controlled trials of fetal monitoring 1973‐1977. In:

Perinatal Medicine

(ed. O. Thalhammer, K. Baumgarten and A. Pollak), 260–265. Stuttgart: George Thieme.

2. MacDonald, D., Grant, A., Sheridan‐Pereira, M. et al. (1985). The Dublin randomized controlled trial of intrapartum fetal heart rate monitoring.

Am. J. Obstet. Gynecol.

152: 524–539.

3. Peto, R. (1978). Clinical trial methodology.

Biomedicine

28 (special issue): 24–36.

4. Peto, R., Pike, M.C., Armitage, P. et al. (1977). Design and analysis of randomized clinical trials requiring prolonged observation of each patient. II. Analysis and examples.

Br. J. Cancer

35: 1–39.

5. Altman, D.G. (1981). Evaluating a series of clinical trials of the same treatment. Unpublished seven‐page summary of the author's presentation at a meeting of the International Epidemiological Association in Edinburgh, August 1981. Available from

jameslindlibrary.org/altman‐dg‐1981.

6. Altman, D.G. (1983). Evaluating a series of clinical trials of the same treatment. Unpublished 40‐page development of the author's seven‐page summary of his presentation at a meeting of the International Epidemiological Association in Edinburgh, August 1981. Available from

jameslindlibrary.org/altman‐dg‐1983.

7. Altman, D.G. (2015). Some reflections on the evolution of meta‐analysis.

Res. Synth. Methods

6: 265–267.

8. Altman, D. (2013). Twenty years of meta‐analysis and evidence synthesis methods: a personal reflection. Cochrane methods.

Cochrane Database Syst. Rev.

2013 (Suppl 1): 2–11.

https://www.cochranelibrary.com/documents/20182/64256496/Cochrane+Methods+2013/0f6dc933‐5d27‐fe40‐6bd9‐dfad13e08e50

.

9. Jenicek, M. (1987).

Méta‐analyse en médecine. Évaluation et synthèse de l'information clinique et épidémiologique

[Meta‐analysis in Medicine: Evaluation and Synthesis of Clinical and Epidemiological Information]. St. Hyacinthe and Paris: EDISEM and Maloine Éditeurs.

10. Mulrow, C.D. (1987). The medical review article: state of the science.

Ann. Intern. Med.

106: 485–488.

11. Oxman, A.D. and Guyatt, G.H. (1988). Guidelines for reading literature reviews.

Can. Med. Assoc. J.

138: 697–703.

12. Chalmers, I., Dickersin, K., and Chalmers, T.C. (1992). Getting to grips with Archie Cochrane's agenda: all randomised controlled trials should be registered and reported.

BMJ

305: 786–788.

13. Smith, R. (1994a). Hearts and minds.

BMJ

309: 3 September.

14. Smith, R. (1994b). Systematic reviews, stupid doctors, and red meat.

BMJ

309: 12 November.

15. Chalmers, I. and Altman, D.G. (1995).

Systematic Reviews

. London: BMJ Books.

16. Egger, M., Davey Smith, G., and Altman, D.G. (2001).

Systematic Reviews in Health Care: Meta‐Analysis in Context

, 2e. London: BMJ Books.

List of Contributors

Douglas G. Altman

Centre for Statistics in Medicine

Nuffield Department of Orthopaedics

Rheumatology and Musculoskeletal Sciences

University of Oxford

Oxford, UK

Gerd Antes

Cochrane Germany (1997–2018)

University of Freiburg

Freiburg, Germany

Julia Bohlius

Institute of Social and Preventive Medicine

University of Bern

Bern, Switzerland;

Swiss Tropical and Public Health Institute

Basel, Switzerlandand

University of Basel

Basel, Switzerland

Michael Borenstein

Director of Biostatistics

Biostat, Inc., Englewood

NJ, USA

Diana Buitrago‐Garcia

Institute of Social and Preventive Medicine

University of Bern

Bern, Switzerland

Iain Chalmers

Centre for Evidence‐Based Medicine

University of Oxford

Oxford, UK

Gary S. Collins

Centre for Statistics in Medicine

Nuffield Department of Orthopaedics

Rheumatology and Musculoskeletal Sciences

University of Oxford, Oxford, UK

Bruno R. da Costa

Institute of Health Policy

Management and Evaluation

University of Toronto

Toronto, Canada

and

Applied Health Research Centre (AHRC)

St. Michael’s Hospital

Toronto, Canada

Thomas P.A. Debray

Julius Center for Health Sciences and Primary Care; Cochrane Netherlands

University Medical Center Utrecht

Utrecht University

Utrecht, The Netherlands

George Davey Smith

Medical Research Council Integrative Epidemiology Unit

Population Health Sciences

Bristol Medical School

University of Bristol

Bristol, UK

Jonathan J. Deeks

Test Evaluation Research Group

Institute of Applied Health Research

University of Birmingham

Birmingham, UK

Olaf M. Dekkers

Department of Clinical Epidemiology

Aarhus University Hospital

Aarhus, Denmark

and

Department of Clinical Epidemiology

Leiden University Medical Centre

Leiden, The Netherlands

Shah Ebrahim

Co‐ordinating Editor, Cochrane Heart Group (1995–2014)

and

London School of Hygiene & Tropical Medicine

University of London

London, UK

Matthias Egger

Institute of Social and Preventive Medicine

University of Bern

Bern, Switzerland

and

Centre for Infectious Diseases Epidemiology and Research

University of Cape Town

South Africa

Julian Elliott

Cochrane Australia

School of Public Health and Preventive Medicine

Monash University

Melbourne, Australia

David J. Fisher

Medical Research Council Clinical Trial Unit

University College London

London, UK

Julie Glanville

Glanville.info

York, UK

Gibran Hemani

Medical Research Council Integrative Epidemiology Unit

Population Health Sciences

Bristol Medical School

University of Bristol

Bristol, UK

Julian P.T. Higgins

Population Health Sciences

Bristol Medical School

University of Bristol

Bristol, UK

and

National Institute of Health Research Applied Research Collaboration West

University Hospitals Bristol and Weston NHS Foundation Trust

Bristol, UK

Mark D. Huffman

Northwestern University Feinberg School of Medicine

Chicago, IL, USA

and

The George Institute for Global Health

University of New South Wales

Sydney, Australia

Brian Hutton

School of Epidemiology and Public Health

University of Ottawa

Ottawa, Canada

and

Clinical Epidemiology Program

Ottawa Hospital Research Institute

Ottawa, Canada

Susanna C. Larsson

Institute of Environmental Medicine

Karolinska Institutet

Stockholm, Sweden

and

Department of Surgical Sciences

Uppsala University

Uppsala, Sweden

Carol Lefebvre

Lefebvre Associates Ltd

Oxford, UK

Tianjing Li

Department of Ophthalmology

School of Medicine

University of Colorado Anschutz Medical Campus

Aurora, CO, USA

Dimitris Mavridis

Department of Primary Education, University of Ioannina

Ioannina, Greece

David Moher

Centre for Journalology

Clinical Epidemiology Program

Ottawa Hospital Research Institute

Ottawa, Canada

and

School of Epidemiology and Public Health

University of Ottawa

Ottawa, Canada

Karel G.M. Moons

Julius Center for Health Sciences and Primary Care; Cochrane

Netherlands

University Medical Center Utrecht

Utrecht University

Utrecht, The Netherlands

Nicola Orsini

Department of Global Public Health

Karolinska Institutet

Stockholm, Sweden

Nancy Owens

Cochrane Central Executive Team (2001–2018)

Fairfax, VA, USA

Matthew J. Page

School of Public Health and Preventive Medicine

Monash University

Melbourne, Australia

Richard D. Riley

Centre for Prognosis Research

School of Medicine

Keele University

Newcastle‐under‐Lyme, UK

Eliane Rohner

Institute of Social and Preventive Medicine

University of BernBern, Switzerland

Georgia Salanti

Institute of Social and Preventive Medicine

University of Bern

Bern, Switzerland

Jelena Savović

Population Health Sciences

Bristol Medical School

University of Bristol

Bristol, UK

and

National Institute of Health Research Applied Research Collaboration West

University Hospitals Bristol and Weston NHS Foundation Trust

Bristol, UK

Guido Schwarzer

Faculty of Medicine and Medical Center

Institute of Medical Biometry and Statistics

University of Freiburg

Freiburg, Germany

Holger J. Schünemann

Department of Health Research Methods, Evidence, and Impact and Department of Medicine

McMaster University

Hamilton, Ontario, Canada

Larissa Shamseer

Knowledge Translation ProgramLi Ka Shing Knowledge Institute

St. Michael’s Hospital

Unity Health Toronto

Toronto, Canada

Mark C. Simmonds

Centre for Reviews and Dissemination

University of York

York, UK

Beverley Shea

Centre for Journalology

Clinical Epidemiology Program

Ottawa Hospital Research Institute

Ottawa, Canada

and

School of Epidemiology and Public Health

University of Ottawa

Ottawa, Canada

Jonathan A.C. Sterne

Population Health Sciences

Bristol Medical School

University of Bristol

Bristol, UK

Lesley A. Stewart

Centre for Reviews and Dissemination

University of York

York, UK

Yemisi Takwoingi

Test Evaluation Research Group

Institute of Applied Health Research

University of Birmingham

Birmingham, UK

David Tovey

Editor in Chief, The Cochrane Library (2009–2019)

Sussex, UK

Sven Trelle

CTU Bern, University of Bern

Bern, Switzerland

Tari Turner

Cochrane Australia

School of Public Health and Preventive Medicine

Monash University

Melbourne, Australia

Ian R. White

MRC Clinical Trials Unit at UCL

London, UK

Penny F. Whiting

Population Health Sciences

Bristol Medical School

University of Bristol

Bristol, UK

Marcel Zwahlen

Institute of Social and Preventive Medicine

University of Bern

Bern, Switzerland