Publication Bias in Meta-Analysis (eBook, PDF)

Prevention, Assessment and Adjustments
Redaktion: Rothstein, Hannah R.; Borenstein, Michael; Sutton, Alexander J.

• Format: PDF

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 374
• Erscheinungstermin: 3. Februar 2006
• Englisch
• ISBN-13: 9780470870150
• Artikelnr.: 38185443

Autorenporträt

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.

Inhaltsangabe

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.

Rezensionen

"The book adopts an inter-disciplinary approach and willmake a useful reference volume for any researchers and graduatestudents who conduct systematic reviews or meta-analyses.University and medical libraries, as well as pharmaceuticalcompanies and government regulatory agencies, will also find thisinvaluable." (Zentralblatt MATH, 2012)"...a well-written book and will be useful for researchers orgraduate students..." (Journal of the American StatisticalAssociation, June 2007)

"...the book definatly succeeds in raising the awareness of thereader to an issue that unfortunately still remainsunderappreciated" (Psychometrika June 2007)

" ... an incredibly thorough, useful book. The impressivelist of contributors ... is the book's particularstrength." (JRSSA, Vol. 169, No. 4, October 2006)

"...a useful introduction to meta-analysisand...state-of-the-art description of the statistical problemsassociated...of particular value as a reference for amulti-disciplinary audience..." (Biometrics, June2006)

"This book will have a wide appeal and is a major contributionto ways of distilling evidence from scientific literature."(Journal of Tropical Pediatrics, June 2006)

"I predict that this book will become the key text in thearea." (Short Book Reviews, April 2006)

"...likely to become a standard reference for those whocarry systematic literature reviews." (Psychometrika, June2007)

"This elegant hardcover book brings together 16 finecontributions and three appendices...It is obviously a must formedical libraries." (Journal of Applied Science,2007)

"A strong and rigorous collection of timely essays that will beuseful for advanced scholars and experienced researchers in variousdisciplines.... It is an obvious must for medical libraries."(Journal of Applied Statistics, December 2007)