Handbook of Meta-Analysis

Meta-analysis is the application of statistics to combine results from multiple studies and draw appropriate inferences. Its use and importance have exploded over the last 25 years as the need for a robust evidence base has become clear in many scientific areas, including medicine and health, social sciences, education, psychology, ecology, and economics.Recent years have seen an explosion of methods for handling complexities in meta-analysis, including explained and unexplained heterogeneity between studies, publication bias, and sparse data. At the same time, meta-analysis has been extended beyond simple two-group comparisons of continuous and binary outcomes to comparing and ranking the outcomes from multiple groups, to complex observational studies, to assessing heterogeneity of effects, and to survival and multivariate outcomes. Many of these methods are statistically complex and are tailored to specific types of data.Key featuresRigorous coverage of the full range of current statistical methodology used in meta-analysisComprehensive, coherent, and unified overview of the statistical foundations behind meta-analysisDetailed description of the primary methods for both univariate and multivariate dataComputer code to reproduce examples in chaptersThorough review of the literature with thousands of referencesApplications to specific types of biomedical and social science dataSupplementary website with code, data, sample chapters, and errataThis book is for a broad audience of graduate students, researchers, and practitioners interested in the theory and application of statistical methods for meta-analysis. It is written at the level of graduate courses in statistics, but will be of interest to and readable for quantitative scientists from a range of disciplines. The book can be used as a graduate level textbook, as a general reference for methods, or as an introduction to specialized topics using state-of-the art methods.

"Handbook of Meta-Analysis is a most laudable and detailed treatise on meta-analysis. It successfully covers – with gusto and substance – the full range of statistical methodology used in meta-analysis in a statistically rigorous and up-to-date manner, exuding a good balance of theory and applications (with real data and software syntax provided). It provides a comprehensive, coherent, and unified overview of the statistical foundations behind meta-analysis. Crafted by experts on the topic, each chapter is written with lucidity and surgical precision. It is elegantly organized, encyclopedic in breadth and depth, and fluent in exposition on the multidimensional role of meta-analysis: core material (background, systematic review process, data extraction, study-level results, frequent and Bayesian approaches); key extensions (meta-regression, individual data, multivariate meta-analysis, network meta-analysis, model checking, bias); and advances in particular fields of biomedical and social research (control risk regression, survival data, correlation matrices, genetic data, dose-response relationships, diagnostic tests, surrogate endpoints, complex observational data, prognostic models). It is a tour de force, a premier, and an indispensable reference that is highly recommended – and a must for serious researchers and practitioners engaged in meta-analysis. This state-of-the-science handbook is destined to be a classic." - Joseph C. Cappelleri, PhD, MPH, MS, Executive Director of Biostatistics, Pfizer Inc "For many researchers in social, medical, life and environmental sciences, it has become an essential part of their activities to synthesize evidence from the body of relevant research. The Handbook of Meta-analysis provides the most comprehensive and up-to-date coverage of the quantitative part of evidence synthesis, i.e., meta-analysis. Therefore, this handbook is a must-have for all researchers who wish to unlock and understand the power and potential of meta-analysis, but also for those who have already found and benefited from it. The authors of this edited volume are an interdisciplinary all-star team of statisticians and methodologists; probably, each of them could have written a textbook on meta-analysis. Here, they introduce both basics and advanced techniques that they have been leading to develop over their career. For many statisticians, a meta-analysis may be just one type of linear models (Chapters 1-11), yet, as this book demonstrates, meta-analyses can come in diverse forms and serve different purposes (see Chapters 14-22). Further, there are specific statistical issues meta-analysis needs to grapple with, such as publication bias (Chapters 12-13). The book ends with a chapter on how to use meta-analysis to plan our future work (Chapter 23) – what all scientists should be doing to reduce research waste and to acc, "The handbook is intended for a relatively wide audience of statisticians, to be used as a textbook in a graduate course, as a reference book, a handbook or an introduction. The first part easily accomplishes these aims. There is a fair number of formulas, but they are well-explained and thus the text should be accessible for any statistician or quantitative researcher."-Anikó Lovik, International Society for Clinical Biostatistics, 72, 2021

Christopher SchmidChristopher H. Schmid is Professor of Biostatistics at Brown University. He received his BA in Mathematics from Haverford College in 1983 and his PhD in Statistics from Harvard University in 1991. In 1991, he joined the Institute for Clinical Research and Health Policy Studies at Tufts Medical Center and joined the medical faculty at Tufts University in 1992. He became the director of the Biostatistics Research Center in 2006 and Associate Director of the Tufts Clinical and Translational Research training program in 2009. In 2012, he moved to Brown University to co-found the Center for Evidence Synthesis in Health. In 2016, he became Director of the Clinical Study Design, Epidemiology and Biostatistics Core of the Rhode Island Center to Advance Translational Science and in 2018 became Chair of Biostatistics in the School of Public Health.Dr. Schmid has a long record of collaborative research and training activities in many different clinical and public health research areas. His research focuses on Bayesian methods for meta-analysis, including networks of treatments and N-of-1 designs, as well as open-source software tools, as well as methods for developing and assessing predictive models using data from multiple databases, e.g., the current standard biomarker prediction tool for GFR, glomerular filtration rate. He is the author of nearly 300 publications, including coauthored consensus CONSORT reporting guidelines for N-of-1 trials and single-case designs, and PRISMA guidelines extensions for meta-analysis of individual participant studies and for network meta-analyses as well as the Institute of Medicine report that established US standards for systematic reviews.Dr. Schmid is an elected member of the Society for Research Synthesis Methodology and co-founding editor of its journal, Research Synthesis Methods. He is a Fellow of the American Statistical Association and long-time statistical editor of the American Journal of Kidney Diseases.Ian WhiteIan White is Professor of Statistical Methods for Medicine at the Medical Research Council Clinical Trials Unit at University College London, UK. He originally studied mathematics at Cambridge University, and his first career was as a teacher of mathematics in The Gambia, Cambridge and London. He obtained his MSc in statistics from University College London, where he subsequently worked in the Department of Epidemiology and Public Health. He was then Senior Lecturer in the Medical Statistics Unit at the London School of Hygiene and Tropical Medicine and for 16 years programme leader at the Medical Research Council Biostatistics Unit in Cambridge. He received his PhD by publication in 2011.His research interests are in statistical methods for the design and analysis of clinical trials, observational studies and meta-analyses. He is particularly interested in developing methods for handling missing data, correcting for departures from randomised treatment, novel trial designs, simulatio