Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments / Edition 1by Hannah R. Rothstein
Pub. Date: 12/05/2005
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.… See more details below
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.
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Table of Contents
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).
Appendix A: Data Sets.
Appendix B: Annotated Bibliography (Hannah R. Rothstein and Ashley Busing).
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