Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves.

Scientific progress often begins with the difficult task of preparing informed, conclusive reviews of existing research. Since the 1970s, the traditional "subjective" approach to research reviewing in the social sciences has been challenged by a statistical alternative known as meta-analysis. Meta-analysis provides a principled method of distilling reliable generalizations from previous studies on a single topic, thereby providing a quantitative and objective background for future research. The Future of Meta-Analysis brings together expert researchers for an in-depth examination of this new methodology—not to promote a consensus view but rather to explore from several perspectives the theories, tensions, and concerns of meta-analysis, and to illustrate through concrete examples the rationale behind meta-analytic decisions. In a meta-analysis prepared especially for this volume, a statistician and a psychologist review the existing literature on aphasia treatment. In a second study, experts analyze six still-unpublished meta-analyses sponsored by the National Institute of Education to investigate the effects of school desegregation on the academic achievement of black children. This unique case study approach provides valuable discussion of the process of meta-analysis and of the current implications of meta-analysis for policy assessment. Prepared under the auspices of the National Research Council, The Future of Meta-Analysis presents a forum for leaders in this rapidly evolving field to discuss salient conceptual and technical issues and to offer a new theoretical framework, further methodological guidance, and statistical innovations that anticipate a future in which meta-analysis will play an even more effective and valuable role in social science research.

1. Provides a comprehensive overview of meta-analysis methods and applications. 2. Divided into four major sub-topics, covering univariate meta-analysis, multivariate, applications and policy. 3. Designed to be suitable for graduate students and researchers new to the field. 4. Includes lots of real examples, with data and software code made available. 5. Chapters written by the leading researchers in the field.

Policymakers, medical practitioners, and the public alike face an increasingly bewildering flood of new and often contradictory scientific studies on almost every topic. Whether the issue is the the best treatment for breast cancer, the need for prenatal food programs to improve the health of poor infants and mothers, or the ability of women to succeed in scientific professions, the healthy growth of modern science has at times done more to stir up controversy than to establish reliable knowledge. But now scientists in several fields have developed a sophisticated new methodology called meta-analysis to address this problem. By numerically combining diverse research findings on a single question, meta-analysis can be used to identify their central tendency and reach conclusions far more reliable than those of any single investigation. How Science Takes Stock vividly tells the story of meta-analysis through the eyes of its architects and champions, and chronicles its history, techniques, achievements, and controversies. Noted science author Morton Hunt visits key practitioners and recounts their use of meta-analysis to resolve important scientific puzzles and longstanding debates. Does psychotherapy work, and if so what form works best? Does spending federal money on education really improve student performance? Can a single enzyme significantly decrease the risk of heart attack? Do boot camps reduce juvenile delinquency? With each account, Hunt illustrates the major components of the meta-analytic method, reveals strategies for resolving practical and theoretical problems, and discusses the impact of meta-analysis on the science and policy communities. In many cases, he demonstrates how meta-analysts have gone a step further to determine the causes of earlier discrepancies. In this way they not only identify successful approaches to the question at hand, but also clarify the conditions under which they will work best. Hunt also portrays the important but frequently controversial business of doing meta-analysis for legislators and government agencies, particularly in sensitive areas of social policy. How Science Takes Stock demonstrates how the statistical techniques of meta-analysis produce more accurate data than the standard literature review or the old-fashioned process of tallying up the results of each scientific study as if they were votes in an election to decide the truth. Hunt also addresses issues of quality control in each phase of the meta-analytic process, and answers skeptics who claim that the dissimilarities between studies are often too significant for meta-analysis to be any more than an apples and oranges approach. This volume conveys the power of meta-analysis to help social policymakers and health professionals resolve their most pressing problems. How Science Takes Stock concludes with a discussion of the future of meta-analysis that examines its potential for further refinements, its growth in the scientific literature, and exciting new possibilities for its future use. An appendix by meta-analysis expert Harris Cooper offers some finer points on the mechanics of conducting a meta-analytic investigation.

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

This title provides step-by-step directions for how to conduct a meta-study, as well as recommendations for tools and standards for the application of this approach.

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