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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