Handbook of Meta-Analysis Chapman & Hall/CRC Handbooks of Modern Statistical Methods Series

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 features

• Rigorous coverage of the full range of current statistical methodology used in meta-analysis
• Comprehensive, coherent, and unified overview of the statistical foundations behind meta-analysis
• Detailed description of the primary methods for both univariate and multivariate data
• Computer code to reproduce examples in chapters
• Thorough review of the literature with thousands of references
• Applications to specific types of biomedical and social science data
• Supplementary website with code, data, sample chapters, and errata

This 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.

1. Introduction to systematic review and meta-analysis
2. General themes in meta-analysis
3. Choice of effect measure and issues in extracting outcome data
4. Analysis of univariate study-level summary data using normal models
5. Exact likelihood methods for group-based summaries
6. Bayesian methods for meta-analysis
7. Meta-regression
8. Individual participant data meta-analysis
9. Multivariate meta-analysis
10. Network meta-analysis
11. Model Checking in meta-analysis
12. Handling internal and external biases: quality and relevance of studies
13. Publication and outcome reporting bias
14. Control risk regression
15. Multivariate meta-analysis of survival proportions
16. Meta-analysis of correlations, correlation matrices and their functions
17. The meta-analysis of genetic studies
18. Meta-analysis of dose-response relationships
19. Meta-analysis of diagnostic tests
20. Meta-analytic approach to evaluation of surrogate endpoints
21. Meta-analysis of epidemiological data, with a focus on individual participant data
22. Meta-analysis of prediction models
23. Using meta-analysis to plan further research

Christopher Schmid

Christopher 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