Applied Meta-Analysis with R - Ding-Geng (Din) Chen

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Biographie:
Ding-Geng (Din) Chen, Ph.D., is a professor at the University of Rochester Medical Center. Dr. Chen has vast experience in biostatistical research and clinical trial development and methodology. He has authored or co-authored more than 100 journal publications on biostatistical methodologies and applications. He is also the co-author (with Dr. Peace) of Clinical Trial Methodology and Clinical Trial Data Analysis Using R and a co-editor (with Drs. Sun and Peace) of Interval-Censored Time-to-Event Data: Methods and Applications. He is a member of the American Statistical Association, chair for the STAT section of the American Public Health Association, an associate editor of the Journal of Statistical Computation and Simulation, and an editorial board member of several other journals. Karl E. Peace, Ph.D., is the Georgia Cancer Coalition Distinguished Cancer Scholar, senior research scientist, and professor of biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University. He is also an adjunct professor of biostatistics at the VCU School of Medicine. Dr. Peace is a reviewer or editor of several journals, the founding editor of the Journal of Biopharmaceutical Statistics, and a fellow of the American Statistical Association. He has authored or co-authored over 150 articles and 10 books. He has received numerous awards, including the University System of Georgia Board of Regents' Alumni Hall of Fame Award, the First President's Medal for outstanding contributions to Georgia Southern University, and distinguished meritorious service awards from the American Public Health Association and other organizations. In 2012, the American Statistical Association created the Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society.

Sommaire:
Introduction to R What Is R? Steps on Installing R and Updating R Packages Database Management and Data Manipulations A Simple Simulation on Multi-Center Studies Summary and Recommendations for Further Reading Research Protocol for Meta-Analyses Introduction Defining the Research Objective Criteria for Identifying Studies to Include in the Meta-Analysis Searching For and Collecting the Studies Data Abstraction and Extraction Meta-Analysis Methods Results Summary and Discussion Fixed Effects and Random Effects in Meta-Analysis Two Datasets from Clinical Studies Fixed-Effects and Random-Effects Models in Meta-Analysis Data Analysis in R Which Model Should We Use? Fixed Effects or Random Effects? Summary and Conclusions Meta-Analysis with Binary Data Meta-Analysis Methods Meta-Analysis of Lamotrigine Studies Discussions Meta-Analysis for Continuous Data Two Published Datasets Methods for Continuous Data Meta-Analysis of Tubeless versus Standard Percutaneous Nephrolithotomy Discussion Heterogeneity in Meta-Analysis Heterogeneity Quantity Q and the Test of heterogeneity in R meta The Quantifying Heterogeneity in R meta Step-By-Step Implementations in R Discussions Meta-Regression Data Meta-Regression Data Analysis Using R Discussion Individual Patient-Level Data Analysis versus Meta-Analysis Introduction Treatment Comparison for Changes in HAMD Treatment Comparison for Changes in MADRS Summary Simulation Study on Continuous Outcomes Discussions Meta-Analysis for Rare Events The Rosiglitazone Meta-Analysis Step-by-Step Data Analysis in R Discussion Other R Packages for Meta-Analysis Combining p-Values in Meta-Analysis R Packages for Meta-Analysis of Correlation Coefficients Multivariate Meta-Analysis Discussions Index