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.

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.

Designed to teach the reader about meta-analysis and show them how to conduct one using SAS. Its focus is more applied and practical than theoretical and will include additional programming codes and examples. Web site to house program code and sample ou

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.

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.

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.

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