Publication Bias in Meta-Analysis Prevention,

Publication bias is the tendency to decide to publish a study based on the results of the study, rather than on the basis of its theoretical or methodological quality. It can arise from selective publication of favorable results, or of statistically significant results. This threatens the validity of conclusions drawn from reviews of published scientific research. Meta-analysis is now used in numerous scientific disciplines, summarizing quantitative evidence from multiple studies. If the literature being synthesised has been affected by publication bias, this in turn biases the meta-analytic results, potentially producing overstated conclusions. Publication Bias in Meta-Analysis examines the different types of publication bias, and presents the methods for estimating and reducing publication bias, or eliminating it altogether. Written by leading experts, adopting a practical and multidisciplinary approach. Provides comprehensive coverage of the topic including: Different types of publication bias, Mechanisms that may induce them, Empirical evidence for their existence, Statistical methods to address them, Ways in which they can be avoided. Features worked examples and common data sets throughout. Explains and compares all available software used for analysing and reducing publication bias. Accompanied by a website featuring software, data sets and further material. Publication Bias in Meta-Analysis adopts an inter-disciplinary approach and will make an excellent reference volume for any researchers and graduate students who conduct systematic reviews or meta-analyses. University and medical libraries, as well as pharmaceutical companies and government regulatory agencies, will also find this invaluable.

Hannah Rothstein is co-chair of the Methods Group of the Campbell Collaboration, and a member of the Collaboration’s Steering Group. She is also a member of the Cochrane Collaboration’s reporting bias methods group. Dr. Rothstein has been first author of four published meta-analyses of employment selection methods and has written many articles on methodological issues in meta-analysis. She has authored a chapter on meta-analysis that appeared in Measuring and Analyzing Behavior in Organizations, and has completed a 25-year retrospective on the contributions of meta-analysis to the field of industrial and organizational psychology that appeared in Validity Generalization: A Critical Review. With Michael Borenstein, and others, she is the author of computer software for meta-analysis and power analysis.

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

Michael Borenstein served as Director of Biostatistics at Hillside Hospital, Long Island Jewish Medical Center from 1980–2002, and as Associate Professor at Albert Einstein College of Medicine from 1992–2002. He has served on various review groups and advisory panels for the National Institutes of Health and as a member of the NIMH Data Safety Monitoring Board, and is an active member of the statistical advisory groups of the Cochrane and Campbell Collaborations. Since the mid-1990s, Dr Borenstein has lectured widely on meta-analysis. He is the PI on several NIH grants to develop software for meta-analysis and is the developer, with Larry Hedges, Julian Higgins, Hannah Rothstein and others, of Comprehensive Meta Analysis, a best-selling computer program for meta-analysis.

Preface.

Acknowledgements.

Notes on Contributors.

Chapter 1: Publication Bias in Meta-Analysis (Hannah R. Rothstein, Alexander J. Sutton and Michael Borenstein).

Part A: Publication bias in context.

Chapter 2: Publication Bias: Recognizing the Problem, Understanding Its Origins and Scope, and Preventing Harm (Kay Dickersin).

Chapter 3: Preventing Publication Bias: Registries and Prospective Meta-Analysis (Jesse A. Berlin and Davina Ghersi).

Chapter 4: Grey Literature and Systematic Reviews (Sally Hopewell, Mike Clarke and Sue Mallett).

Part B: Statistical methods for assessing publication bias.

Chapter 5: The Funnel Plot (Jonathan A.C. Sterne, Betsy Jane Becker and Matthias Egger).

Chapter 6: Regression Methods to Detect Publication and Other Bias in Meta-Analysis (Jonathan A.C. Sterne and Matthias Egger).

Chapter 7: Failsafe N or File-Drawer Number (Betsy Jane Becker).

Chapter 8: The Trim and Fill Method (Sue Duval).

Chapter 9: Selection Method Approaches (Larry V. Hedges and Jack Vevea).

Chapter 10: Evidence Concerning the Consequences of Publication and Related Biases (Alexander J. Sutton).

Chapter 11: Software for Publication Bias (Michael Borenstein).

Part C: Advanced and emerging approaches.

Chapter 12: Bias in Meta-Analysis Induced by Incompletely Reported Studies (Alexander J. Sutton and Therese D. Pigott).

Chapter 13: Assessing the Evolution of Effect Sizes over Time (Thomas A. Trikalinos and John P.A. Ioannidis).

Chapter 14: Do Systematic Reviews Based on Individual Patient Data Offer a Means of Circumventing Biases Associated with Trial Publications? (Lesley Stewart, Jayne Tierney and Sarah Burdett).

Chapter 15: Differentiating Biases from Genuine Heterogeneity: Distinguishing Artifactual from Substantive Effects (John P.A. Ioannidis).

Chapter 16: Beyond Conventional Publication Bias: Other Determinants of Data Suppression (Scott D. Halpern and Jesse A. Berlin).

Appendices.

Appendix A: Data Sets.

Appendix B: Annotated Bibliography (Hannah R. Rothstein and Ashley Busing).

Glossary.

Index.

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Publication Bias in Meta-Analysis Prevention, Assessment and Adjustments

Summary

Author Biography

Table of Contents

Supplemental Materials