Applied Meta-Analysis with R and Stata

Ding-Geng (Din) Chen is a fellow of American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina-Chapel Hill, USA. Formerly, he was a Professor of Biostatistics at the University of Rochester, New York, USA, the Karl E. Peace Endowed Eminent Scholar Chair and professor in Biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University, USA, and a professor of statistics at South Dakota Stata University, USA. Dr. Chen’s research interests include clinical trial biostatistical methodological development in Bayesian models, survival analysis, multi-level modelling and longitudinal data analysis, and statistical meta-analysis. He has published more than 200 refereed papers and co-authored/co-edited 30 book in statistics. Karl E. Peace is the Georgia Cancer Coalition Distinguished Cancer Scholar, Founding Director of the Center for Biostatistics, Professor of Biostatistics, and Senior Research Scientist in the Jiann-Ping Hsu College of Public Health at Georgia Southern University (GSU). Dr. Peace has made pivotal contributions in the development and approval of drugs to treat numerous diseases and disorders. A fellow of the ASA, he has been a recipient of many honors, including the Drug Information Association Outstanding Service Award, the American Public Health Association Statistics Section Award, The First recipient of the President’s Medal for outstanding contributions to GSU, and recognition by the Georgia and US Houses of Representatives, and the Virginia House of Delegates.

• 1. Introduction to R and Stata for Meta-Analysis2. Research Protocol for Meta-Analyses3. Fixed-E ects and Random-E ects in Meta-Analysis4. Meta-Analysis with Binary Data5. Meta-Analysis for Continuous Data6. Heterogeneity in Meta-Analysis7. Meta-Regression8. Multivariate Meta-Analysis9. Publication Bias in Meta-Analysis10. Strategies to Handle Missing Data in Meta-Analysis11. Meta-Analysis for Evaluating Diagnostic Accuracy12. Network Meta-Analysis13. Meta-Analysis for Rare Events14. Meta-Analyses with Individual Patient-Level Data versus Summary Statistics15. Other R/Stata Packages for Meta-Analysis

"The strengths of the second edition continue those of the first edition... A summary and discussion close the chapters, providing professionally generous recommendations for additional reading, software, and websites. Clearly, an applied hands-on approach intended to facilitate quickly moving readers to performing informed meta-data analyses."- Thomas E. Bradstreet, Journal of Biopharmaceutical Statistics, July 2022