Meta Analysis With R

Author: Guido Schwarzer
Editor: Springer
ISBN: 3319214160
File Size: 21,44 MB
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This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.

Applied Meta Analysis With R

Author: Ding-Geng (Din) Chen
Editor: CRC Press
ISBN: 1466505990
File Size: 71,81 MB
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In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R. Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data. Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R) in public health, medical research, governmental agencies, and the pharmaceutical industry.

Applied Meta Analysis With R And Stata

Author: Ding-Geng (Din) Chen
Editor: CRC Press
ISBN: 0429592175
File Size: 56,18 MB
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Review of the First Edition: The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis... A useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs. —Journal of Applied Statistics Statistical Meta-Analysis with R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step by step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What’s New in the Second Edition: Adds Stata programs along with the R programs for meta-analysis Updates all the statistical meta-analyses with R/Stata programs Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs MA-SS Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

Doing Meta Analysis With R

Author: Mathias Harrer
Editor: CRC Press
ISBN: 9780367610074
File Size: 42,90 MB
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This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including 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, SEM meta-analysis are also covered. A companion R package, dmetar, is introduced in 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. Key Features: - Contains two introductory chapters on how to set up an R environment and do basic imports/manipulation 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.

Introduction To Meta Analysis

Author: Michael Borenstein
Editor: John Wiley & Sons
ISBN: 1119964377
File Size: 72,93 MB
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This book provides a clear and thorough introduction to meta-analysis, the process of synthesizing data from a series of separate studies. Meta-analysis has become a critically important tool in fields as diverse as medicine, pharmacology, epidemiology, education, psychology, business, and ecology. Introduction to Meta-Analysis: 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 Clarifies concepts using text and figures, followed by formulas and examples Explains how to avoid common mistakes in meta-analysis Discusses controversies in meta-analysis Features a web site with additional material and exercises A superb combination of lucid prose and informative graphics, written by four of the world’s leading experts on all aspects of meta-analysis. Borenstein, Hedges, Higgins, and Rothstein 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, who used pre-publication versions of some of the chapters, 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 Introduction to Meta-Analysis is an excellent resource for novices and experts alike. The book provides a clear and comprehensive presentation of all basic and most advanced approaches to meta-analysis. This book will be referenced for decades. Michael A. McDaniel, Professor of Human Resources and Organizational Behavior, Virginia Commonwealth University

Methods For Meta Analysis In Medical Research

Author: A. J. Sutton
Editor: Wiley-Blackwell
ISBN:
File Size: 59,27 MB
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Major text including chapters on the following: defining outcome measures; assessing heterogeneity; using fixed effects methods and random effects models for combining study estimates; publication bias.

Meta Analysis

Author: Mike W.-L. Cheung
Editor: John Wiley & Sons
ISBN: 1119993431
File Size: 67,43 MB
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Presents a novel approach to conducting meta–analysis using structural equation modeling. Structural equation modeling (SEM) and meta–analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta–analytic data within the SEM framework, and illustrates how to conduct meta–analysis using the metaSEM package in the R statistical environment. Meta–Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta–analysis in answering research questions. Key ideas in meta–analysis and SEM are briefly reviewed, and various meta–analytic models are then introduced and linked to the SEM framework. Fixed–, random–, and mixed–effects models in univariate and multivariate meta–analyses, three–level meta–analysis, and meta–analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta–analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta–analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta–analysis will be helpful in understanding the materials in this book.

From Experimental Network To Meta Analysis

Author: David Makowski
Editor: Springer
ISBN: 940241696X
File Size: 13,36 MB
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This book has been designed as a methodological guide and shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science.

A Meta Analysis Of Interpersonal Communication Constructivist Research

Author: Lance R. Angell
Editor:
ISBN:
File Size: 65,38 MB
Format: PDF
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Reliability In Cognitive Neuroscience

Author: William R. Uttal
Editor: MIT Press
ISBN: 0262018527
File Size: 65,68 MB
Format: PDF
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A review of the empirical evidence shows that unreliability of research findings relating brain images and cognitive processes is widespread in cognitive neuroscience. Cognitive neuroscientists increasingly claim that brain images generated by new brain imaging technologies reflect, correlate, or represent cognitive processes. In this book, William Uttal warns against these claims, arguing that, despite its utility in anatomic and physiological applications, brain imaging research has not provided consistent evidence for correlation with cognition. Uttal bases his argument on an extensive review of the empirical literature, pointing to variability in data not only among subjects within individual experiments but also in the new meta-analytical approach that pools data from different experiments. This inconsistency of results, he argues, has profound implications for the field, suggesting that cognitive neuroscientists have not yet proven their interpretations of the relation between brain activity captured by macroscopic imaging techniques and cognitive processes; what may have appeared to be correlations may have only been illusions of association. He supports the view that the true correlates are located at a much more microscopic level of analysis: the networks of neurons that make up the brain. Uttal carries out comparisons of the empirical data at several levels of data pooling, including the meta-analytical. He argues that although the idea seems straightforward, the task of pooling data from different experiments is extremely complex, leading to uncertain results, and that little is gained by it. Uttal's investigation suggests a need for cognitive neuroscience to reevaluate the entire enterprise of brain imaging-cognition correlational studies.

Meta Analytic Procedures For Social Research

Author: Robert Rosenthal
Editor: SAGE
ISBN: 9780803942462
File Size: 17,45 MB
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Considers meta-analytic procedures (the quantitative summary of a research domain) in sufficient detail for readers either to carry them out for themselves, or evaluate the procedures when used by others and offers advice about the applicability of these techniques to specific research questions.

Publication Bias In Meta Analysis

Author: Hannah R. Rothstein
Editor: John Wiley & Sons
ISBN: 047087015X
File Size: 68,15 MB
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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.

Practical Meta Analysis

Author: Mark W. Lipsey
Editor: SAGE Publications, Incorporated
ISBN:
File Size: 27,93 MB
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What research designs and methodological features qualify a study for meta-analysis and which disqualify it? What types of research reports are appropriate for the meta-analysis? What is the cultural and linguistic range of the studies to be included? By integrating and translating the current methodological and statistical work into a practical guide, the authors address these questions to provide readers with a state-of-the-art introduction to the various approaches to doing meta-analysis.

Meta Analysis A Comparison Of Approaches

Author: Ralf Schulze
Editor: Hogrefe Publishing
ISBN: 1616762802
File Size: 16,44 MB
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Meta-analysis has become the standard method for summarizing research findings in many scientific fields. The number of published applications using the method has been steadily growing in the last 25 years and the statistical procedures of meta-analysis continue to become more and more advanced.

Meta Analysis

Author: John Edward Hunter
Editor: SAGE Publications, Incorporated
ISBN: 9780803918641
File Size: 37,39 MB
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Meta-analysis is a way of synthesizing previous research on a subject in order to assess what has already been learned, and even to derive new conclusions from the mass of already researched data. In the opinion of many social scientists, it offers hope for a truly cumulative social scientific knowledge.

A Meta Analysis Of Outcome Studies In Long Term Psychodynamic Psychotherapy And Psychoanalysis

Author: William Kemp Lamb
Editor:
ISBN:
File Size: 52,64 MB
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"This meta-analysis synthesizes quantitatively the extant empirical literature pertaining to the effectiveness of treatment in outcome studies of long-term psychodynamic psychotherapy and psychoanalysis."--Abstract.

Enhancement Of Sas And R For Meta Analysis Of Observational Studies

Author: Deedra Rae Nicolet
Editor:
ISBN:
File Size: 23,96 MB
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Abstract: Meta-analysis is used to get an overall effect estimate from a collection of previously conducted studies on the same subject. When this method first became popular, it was in the setting of clinical trials. Meta-analysis identifies homogeneity of effect estimates between studies. This type of analysis also takes into consideration confounding factors and decreases their effect on the estimate. Meta-analysis consists of searching the literature for relevant studies, deciding which studies to include in the analysis, extracting relevant information and analyzing the data. The goal of meta-analysis is to estimate the effect estimate from a collection of relevant studies. In order to find this estimate, we consider two possible models for the data. The data could be fit using a fixed-effects or random-effects model. The fixed- effects model has two popular methods of finding the estimate that we will consider here, the Mantel-Haenszel method and the confidence interval method. This model is based on the assumption that there is only a within study variance component. The randomeffects model is the second model that we address and in this case, we examine the DerSimoman-Laird method for estimating the effect estimate. There is an additional variance component that is used in the random-effects model. This variance component addresses the variance between studies. Meta-analysis has been extensively used with clinical trials and observational studies. In available statistical software, the programs available for meta-analysis can only be used with clinical trials. The goal of this work was to modify the programs in R and SAS, so they would be suitable for use with observational studies. Using data on aspirin use and its effect on colon cancer, we demonstrate the use of the modified R and SAS code. The results presented here demonstrated how we extended the programs for metaanalysis of clinical trials to observational studies. As these are very common in medical studies, this development will allow additional analysis to be done when they are the object of a given study. The hope for this work is to provide researchers with programs suitable for meta-analysis with observational studies and clinical trials.

Meta Analysis And Research Integration Of Regression Studies

Author: Maureen Sue Ash
Editor:
ISBN:
File Size: 66,46 MB
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Meta Analysis For Explanation

Author: Heidi Hartmann
Editor: Russell Sage Foundation
ISBN: 1610441338
File Size: 36,87 MB
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Social science research often yields conflicting results: Does juvenile delinquent rehabilitation work? Is teenage pregnancy prevention effective? In an effort to improve the value of research for shaping social policy, social scientists are increasingly employing a powerful technique called meta-analysis. By systematically pulling together findings of a particular research problem, meta-analysis allows researchers to synthesize the results of multiple studies and detect statistically significant patterns among them. Meta-Analysis for Explanation brings exemplary illustrations of research synthesis together with expert discussion of the use of meta-analytic techniques. The emphasis throughout is on the explanatory applications of meta-analysis, a quality that makes this casebook distinct from other treatments of this methodology. The book features four detailed case studies by Betsy Jane Becker, Elizabeth C. Devine, Mark W. Lipsey, and William R. Shadish, Jr. These are offered as meta-analyses that seek both to answer the descriptive questions to which research synthesis is traditionally directed in the health and social sciences, and also to explore how a more systematic method of explanation might enhance the policy yield of research reviews. To accompany these cases, a group of the field's leading scholars has written several more general chapters that discuss the history of research synthesis, the use of meta-analysis and its value for scientific explanation, and the practical issues and challenges facing researchers who want to try this new technique. As a practical resource, Meta-Analysis for Explanation guides social scientists to greater levels of sophistication in their efforts to synthesize the results of social research. "This is an important book...[it is] another step in the continuing exploration of the wider implications and powers of meta-analytic methods." —Contemporary Psychology

The Nursing Profession

Author: Norma L. Chaska
Editor: Mosby Incorporated
ISBN: 9780801660672
File Size: 22,89 MB
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