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Conducting Meta-Analysis Using SAS

Conducting Meta-Analysis Using SAS
Author: Winfred Arthur, Jr.
Publisher: Psychology Press
Total Pages: 200
Release: 2001-06-01
Genre: Psychology
ISBN: 1135643458
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Conducting Meta-Analysis Using SAS reviews the meta-analysis statistical procedure and shows the reader how to conduct one using SAS. It presents and illustrates the use of the PROC MEANS procedure in SAS to perform the data computations called for by the two most commonly used meta-analytic procedures, the Hunter & Schmidt and Glassian approaches. This book serves as both an operational guide and user's manual by describing and explaining the meta-analysis procedures and then presenting the appropriate SAS program code for computing the pertinent statistics. The practical, step-by-step instructions quickly prepare the reader to conduct a meta-analysis. Sample programs available on the Web further aid the reader in understanding the material. Intended for researchers, students, instructors, and practitioners interested in conducting a meta-analysis, the presentation of both formulas and their associated SAS program code keeps the reader and user in touch with technical aspects of the meta-analysis process. The book is also appropriate for advanced courses in meta-analysis psychology, education, management, and other applied social and health sciences departments.

Integrating Results Through Meta-analytic Review Using SAS Software

Integrating Results Through Meta-analytic Review Using SAS Software
Author: Morgan C. Wang
Publisher: SAS Press
Total Pages: 0
Release: 1999
Genre: Mathematical statistics
ISBN: 9781580252935
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Finally ... a book that addresses the various needs, concepts, and approaches for SAS users who work with meta-analytic procedures! Wang and Bushman introduce you to the important concepts in meta-analysis and how to use SAS software for this specific type of analysis. They describe the meta-analytic (or quantitative) approach to reviewing results from a collection of studies that all investigate the same phenomenon. The authors thoroughly describe how you can use meta-analysis in "data-mining" projects to discover meaningful relations among variables in a collection of studies. Practicing meta-analysts or anyone interested in combining the results from several related studies, surveys, and experiments will benefit from this comprehensive book. It is assumed that the reader has an understanding of meta-analytic procedures and SAS software. Book jacket.

Enhancement of SAS and R for Meta-analysis of Observational Studies

Enhancement of SAS and R for Meta-analysis of Observational Studies
Author: Deedra Rae Nicolet
Publisher:
Total Pages: 138
Release: 2006
Genre: Meta-analysis
ISBN:
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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 of Controlled Clinical Trials

Meta-Analysis of Controlled Clinical Trials
Author: Anne Whitehead
Publisher: John Wiley & Sons
Total Pages: 352
Release: 2003-01-17
Genre: Mathematics
ISBN: 0470854197
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Over the last twenty years there has been a dramatic upsurge in the application of meta-analysis to medical research. This has mainly been due to greater emphasis on evidence-based medicine and the need for reliable summaries of the vast and expanding volume of clinical research. At the same time there have been great strides in the development and refinement of the associated statistical methodology. This book describes the planning, conduct and reporting of a meta-analysis as applied to a series of randomized controlled clinical trials. * The various approaches are presented within a general unified framework. * Meta-analysis techniques are described in detail, from their theoretical development through to practical implementation. * Each topic discussed is supported by detailed worked examples. * A comparison of fixed and random effects approaches is included, as well as a discussion of Bayesian methods and cumulative meta-analysis. * Fully documented programs using standard statistical procedures in SAS are available on the Web. Ideally suited for practising statisticians and statistically-minded medical professionals, the book will also be of use to graduate students of medical statistics. The book is a self-contained and comprehensive account of the subject and an essential purchase for anyone involved in clinical trials.

Meta-Analysis with R

Meta-Analysis with R
Author: Guido Schwarzer
Publisher: Springer
Total Pages: 256
Release: 2015-10-08
Genre: Medical
ISBN: 3319214160
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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.

Analysis of Observational Health Care Data Using SAS

Analysis of Observational Health Care Data Using SAS
Author: Douglas E. Faries
Publisher: SAS Press
Total Pages: 0
Release: 2010
Genre: Medical care
ISBN: 9781607642275
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This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.

Introduction to Meta-Analysis

Introduction to Meta-Analysis
Author: Michael Borenstein
Publisher: John Wiley & Sons
Total Pages: 350
Release: 2011-08-24
Genre: Medical
ISBN: 1119964377
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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

A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling

A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling
Author: Larry Hatcher
Publisher: SAS Institute
Total Pages: 444
Release: 2013-03-01
Genre: Computers
ISBN: 1612903878
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Annotation Structural equation modeling (SEM) has become one of the most important statistical procedures in the social and behavioral sciences. This easy-to-understand guide makes SEM accessible to all userseven those whose training in statistics is limited or who have never used SAS. It gently guides users through the basics of using SAS and shows how to perform some of the most sophisticated data-analysis procedures used by researchers: exploratory factor analysis, path analysis, confirmatory factor analysis, and structural equation modeling. It shows how to perform analyses with user-friendly PROC CALIS, and offers solutions for problems often encountered in real-world research. This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

Doing Meta-Analysis with R

Doing Meta-Analysis with R
Author: Mathias Harrer
Publisher: CRC Press
Total Pages: 500
Release: 2021-09-15
Genre: Mathematics
ISBN: 1000435636
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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

Modern Approaches to Clinical Trials Using SAS

Modern Approaches to Clinical Trials Using SAS
Author: Sandeep Menon
Publisher: SAS Institute
Total Pages: 482
Release: 2015-12-09
Genre: Computers
ISBN: 1629600822
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Get the tools you need to use SAS® in clinical trial design! Unique and multifaceted, Modern Approaches to Clinical Trials Using SAS: Classical, Adaptive, and Bayesian Methods, edited by Sandeep M. Menon and Richard C. Zink, thoroughly covers several domains of modern clinical trial design: classical, group sequential, adaptive, and Bayesian methods that are applicable to and widely used in various phases of pharmaceutical development. Written for biostatisticians, pharmacometricians, clinical developers, and statistical programmers involved in the design, analysis, and interpretation of clinical trials, as well as students in graduate and postgraduate programs in statistics or biostatistics, the book touches on a wide variety of topics, including dose-response and dose-escalation designs; sequential methods to stop trials early for overwhelming efficacy, safety, or futility; Bayesian designs that incorporate historical data; adaptive sample size re-estimation; adaptive randomization to allocate subjects to more effective treatments; and population enrichment designs. Methods are illustrated using clinical trials from diverse therapeutic areas, including dermatology, endocrinology, infectious disease, neurology, oncology, and rheumatology. Individual chapters are authored by renowned contributors, experts, and key opinion leaders from the pharmaceutical/medical device industry or academia. Numerous real-world examples and sample SAS code enable users to readily apply novel clinical trial design and analysis methodologies in practice.