Publication Bias In Meta Analysis
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Publication Bias In Meta Analysis
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Author : Hannah R. Rothstein
language : en
Publisher: John Wiley & Sons
Release Date : 2006-02-03
Publication Bias In Meta Analysis written by Hannah R. Rothstein and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-03 with Mathematics categories.
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.
Doing Meta Analysis With R
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Author : Mathias Harrer
language : en
Publisher: CRC Press
Release Date : 2021-09-15
Doing Meta Analysis With R written by Mathias Harrer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-15 with Mathematics categories.
Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Correcting For Publication Bias In The Presence Of Covariates
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Author : U. S. Department of Health and Human Services
language : en
Publisher: Createspace Independent Pub
Release Date : 2013-05-17
Correcting For Publication Bias In The Presence Of Covariates written by U. S. Department of Health and Human Services and has been published by Createspace Independent Pub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-17 with Medical categories.
Meta-analysis has become increasingly popular in the last decade and is now in the top position on most proposed hierarchies of evidence. Meta-analyses are also the most cited study design in the health sciences literature. The traditional role for meta-analysis has been to compile information from diverse studies on the same topic, thus increasing power. However, there is increasing recognition of the challenges that heterogeneity presents in data synthesis. It is important to quantify, assess, and potentially interpret heterogeneity and try to distinguish between genuine between-study heterogeneity and biases. Meta-analytic studies provide a very useful tool for sensitizing researchers, physicians, and public health practitioners to the almost ubiquitous presence of biases in research. The focus in the present report is on one such bias, publication bias, which has been suggested as the most important threat to the validity of meta-analyses. The trim and fill method was developed by Duval and Tweedie to address the issue of publication bias in meta-analysis, and it is now widely used in practice in many areas of public health research. It relies on scrutinizing a funnel plot for asymmetry, assumed to be a manifestation of publication bias (see below). The goal of this research was to extend the trim and fill method to those situations where covariates are available as possible explanations for some of the funnel plot asymmetry. Publication bias is the term used for the bias that may occur when the research on a particular topic does not include the whole population of studies that has been performed. The danger to interpretation under these conditions is that the wrong conclusions may be drawn if the available studies differ systematically from the results of all the research that has been done. Publication bias is a phenomenon that runs counter to the way in which the scientific method has developed over the past century. One of the key historical contributions of statistical thinking has been a move away from a context where possible random observations were acceptable, to one where only those results that are statistically significant (i.e., not due to chance alone) are seen as being established and worth consideration. It is commonly believed that studies are not uniformly likely to be published in scientific journals. Easterbrook et al. suggested that statistical significance is a major determining factor of publication. Some researchers may not submit a nonsignificant result for publication, and editors may fail to publish nonsignificant results even if they are submitted. Therefore, there may be a nonrepresentative proportion of significant studies in the scientific literature. This becomes problematic for a meta-analysis in which data come solely from the published literature, potentially leading to a nonrepresentative proportion of significant studies in the meta-analysis dataset. A standard meta-analysis will then result in a conclusion biased toward significance. This is not just an academic problem: it can ultimately influence the clinical decisions of medical practitioners and public health officials. As Glass noted in 1976, journals that directly or indirectly influence medical practice cannot afford to ignore this problem. He claims, “The potential for publication bias concerns us because physicians now will search systematic reviews to determine the best treatment for patients. If positive results get published more, then the risk of adopting ineffective and even harmful medical practices is greater.”
Effects Of Publication Bias In Meta Analysis
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Author : Susan Duval
language : en
Publisher:
Release Date : 1999
Effects Of Publication Bias In Meta Analysis written by Susan Duval and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
A Combination Of Tests And Publication Bias In Meta Analysis
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Author : Madhuchhanda Mazumdar
language : en
Publisher:
Release Date : 1991
A Combination Of Tests And Publication Bias In Meta Analysis written by Madhuchhanda Mazumdar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with categories.
Introduction To Meta Analysis
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Author : Michael Borenstein
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-19
Introduction To Meta Analysis written by Michael Borenstein and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-19 with Medical categories.
A clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. The first edition of this text was widely acclaimed for the clarity of the presentation, and quickly established itself as the definitive text in this field. The fully updated second edition includes new and expanded content on avoiding common mistakes in meta-analysis, understanding heterogeneity in effects, publication bias, and more. Several brand-new chapters provide a systematic “how to” approach to performing and reporting a meta-analysis from start to finish. Written by four of the world’s foremost authorities on all aspects of meta-analysis, the new edition: Outlines the role of meta-analysis in the research process Shows how to compute effects sizes and treatment effects Explains the fixed-effect and random-effects models for synthesizing data Demonstrates how to assess and interpret variation in effect size across studies Explains how to avoid common mistakes in meta-analysis Discusses controversies in meta-analysis Includes access to a companion website containing videos, spreadsheets, data files, free software for prediction intervals, and step-by-step instructions for performing analyses using Comprehensive Meta-Analysis (CMA)™ Download videos, class materials, and worked examples at www.Introduction-to-Meta-Analysis.com This book offers the reader a unified framework for thinking about meta-analysis, and then discusses all elements of the analysis within that framework. The authors address a series of common mistakes and explain how to avoid them. As the editor-in-chief of the American Psychologist and former editor of Psychological Bulletin, I can say without hesitation that the quality of manuscript submissions reporting meta-analyses would be vastly better if researchers read this book. Harris Cooper, Hugo L. Blomquist Distinguished Professor Emeritus of Psychology and Neuroscience, Editor-in-chief of the American Psychologist, former editor of Psychological Bulletin A superb combination of lucid prose and informative graphics, the authors provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students raved about the clarity of the explanations and examples. David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics. The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice. Jesse A. Berlin, ScD This book offers the reader a unified framework for thinking about meta-analysis, and then discusses all elements of the analysis within that framework. The authors address a series of common mistakes and explain how to avoid them. As the editor-in-chief of the American Psychologist and former editor of Psychological Bulletin, I can say without hesitation that the quality of manuscript submissions reporting meta-analyses would be vastly better if researchers read this book. Harris Cooper, Hugo L. Blomquist Distinguished Professor Emeritus of Psychology and Neuroscience, Editor-in-chief of the American Psychologist, former editor of Psychological Bulletin A superb combination of lucid prose and informative graphics, the authors provide a refreshing departure from cookbook approaches with their clear explanations of the what and why of meta-analysis. The book is ideal as a course textbook or for self-study. My students raved about the clarity of the explanations and examples. David Rindskopf, Distinguished Professor of Educational Psychology, City University of New York, Graduate School and University Center, & Editor of the Journal of Educational and Behavioral Statistics. The approach taken by Introduction to Meta-analysis is intended to be primarily conceptual, and it is amazingly successful at achieving that goal. The reader can comfortably skip the formulas and still understand their application and underlying motivation. For the more statistically sophisticated reader, the relevant formulas and worked examples provide a superb practical guide to performing a meta-analysis. The book provides an eclectic mix of examples from education, social science, biomedical studies, and even ecology. For anyone considering leading a course in meta-analysis, or pursuing self-directed study, Introduction to Meta-analysis would be a clear first choice. Jesse A. Berl
Individual Participant Data Meta Analysis
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Author : Richard D. Riley
language : en
Publisher: John Wiley & Sons
Release Date : 2021-06-08
Individual Participant Data Meta Analysis written by Richard D. Riley and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-08 with Medical categories.
Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points. Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data. Intended for a broad audience, the book will enable the reader to: Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review Recognise the scope, resources and challenges of IPD meta-analysis projects Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators Understand how to obtain, check, manage and harmonise IPD from multiple studies Examine risk of bias (quality) of IPD and minimise potential biases throughout the project Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research Critically appraise existing IPD meta-analysis projects Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction models Detailed examples and case studies are provided throughout.
Meta Analysis In Medical Research
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Author : Gioacchino Leandro
language : en
Publisher: John Wiley & Sons
Release Date : 2008-04-15
Meta Analysis In Medical Research written by Gioacchino Leandro and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-15 with Medical categories.
This book is a joint package of a practical manual on how to undertake meta-analysis in medicine together with an accompanying CD-ROM. This provides individuals with access to meta-analysis software and the instructions and guidance on how to undertake them. The software package contains a computer program 'Metanalysis' which performs statistical analyses for the meta-analysis. It has some unique features currently not available in other meta-analysis software packages: ability to import graphics into Word, PowerPoint etc Galbraith's plots cumulative meta-analysis number needed to treat publication bias assessment The graphics generated by the software are in a format compatible with Microsoft PowerPoint. Click here to view sample graphics. Order your copy online today!
Methods Of Meta Analysis
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Author : Frank L. Schmidt
language : en
Publisher: SAGE Publications
Release Date : 2014-02-05
Methods Of Meta Analysis written by Frank L. Schmidt and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-05 with Social Science categories.
Designed to provide researchers clear and informative insight into techniques of meta-analysis, the Third Edition of Methods of Meta-Analysis: Correcting Error and Bias in Research Findings is the most comprehensive text on meta-analysis available today. It is the only book that presents a full and usable treatment of the role of study artifacts in distorting study results, as well as methods for correcting results for such biases and errors. Meta-analysis is arguably the most important methodological innovation in the last thirty-five years, due to its immense impact on the development of cumulative knowledge and professional practice. This text, now in its updated Third Edition, has been revised to cover the newest developments in meta-analysis methods, evaluation, correction, and more. This reader-friendly book is the definitive resource on meta-analysis. “This text is the primary source text for psychometric meta-analysis methods.” —Emily E. Tanner-Smith, Vanderbilt University “The key strength of the book is the complete and thorough coverage of psychometric meta-analysis. This technique is not covered in any other meta-analysis text, and is a major contribution to the literature…The meta-analysis field needs to find ways to integrate Hunter and Schmidt’s methods into current meta-analysis practice.” —Terri D. Pigott, Loyola University of Chicago “This is an important text. It is the only book that presents adequate coverage of psychometric meta-analysis. In addition to its use as a textbook, it is an invaluable resource for anyone involved in meta-analytic studies.” —Steven Pulos, University of Northern Colorado
Detecting And Correcting Publication Bias In Meta Analysis
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Author : Xin Li (M.S. in Statistics, M.A.)
language : en
Publisher:
Release Date : 2009
Detecting And Correcting Publication Bias In Meta Analysis written by Xin Li (M.S. in Statistics, M.A.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Publication bias (PB) makes the resources for meta-analysis (M-A) unreliable in the sense of completion and accuracy, so to investigate, identify and correct PB is a very important issue in M-A. The current study proposed an empirical comparison in both detection and correcting PB, using a Monte Carlo study. Conditions to be manipulated include the number of primary studies, number of missing studies and true effect size. RANNOR in SAS will be used to generate normally distributed random variables and, for each condition, 10,000 M-As will be simulated. Type I error rates are to be calculated for the conditions with no PB and powers were estimated for the conditions with PB and adequate type I error control. Finally, a demonstration of how M-A can and should be used as a part of program evaluations was given.