Applied Meta-Analysis with R
Chapman and Hall/CRC – 2013 – 342 pages
In biostatistical research and courses, practitioners and students often lack a thorough understanding of how to apply statistical methods to synthesize biomedical and clinical trial data. Filling this knowledge gap, Applied Meta-Analysis with R shows how to implement statistical meta-analysis methods to real data using R.
Drawing on their extensive research and teaching experiences, the authors provide detailed, step-by-step explanations of the implementation of meta-analysis methods using R. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R packages and functions. This systematic approach helps readers thoroughly understand the analysis methods and R implementation, enabling them to use R and the methods to analyze their own meta-data.
Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R) in public health, medical research, governmental agencies, and the pharmaceutical industry.
"Chen and Peace’s book adds to a growing number of resources for practitioners of meta-analysis that include short courses, specialty software, and textbooks devoted to the subject. What distinguishes Applied Meta-Analysis with R(AMAR) is its focus on the use of R, the current language of choice for many biostatisticians and students of biostatistics. Chen and Peace’s writing style mixes explanatory text with numerous step-by-step programming examples. The examples are taken from real clinical applications, including Dr. Steven Nissen’s controversial synthesis of rosiglitazone trials (2007). The examples that pepper the text help to demonstrate the usefulness of meta-analysis, while also addressing some of the practical challenges, such as rare event data, that can arise in real applications. … As the first applied text on meta-analysis in R, practitioners will find AMAR a useful though imperfect attempt to fill an important gap in their library."
—Journal of Biopharmaceutical Statistics, 2015
"Various primers on research synthesis have been written in the past decade, but probably none with such a clear emphasis on software application. The authors strive to reduce theory to a minimum, which makes it a self-learning text that is comprehensible for biologists, physicians, etc. who lack an advanced mathematics background. Unlike in many other textbooks, R is not introduced with meaningless toy examples; instead the reader is taken by the hand and shown around some analyses, graphics, and simulations directly relating to meta-analysis. … A strength of the book, especially from an applied user’s point of view, is that the authors do not get lost in technical details. … a useful hands-on guide for practitioners who want to familiarize themselves with the fundamentals of meta-analysis and get started without having to plough through theorems and proofs."
—Journal of Applied Statistics, 2014
"… especially valuable to medical researchers in universities, industries, or governmental agencies. For beginners who are not familiar with the R system and meta-analysis, this book can also serve as a good guide and reference … an outstanding feature of this book is that it presents plenty of concise R codes and corresponding outputs, with clear comments explaining the meaning of the codes. Currently, a great deal of literature has been devoted to meta-analysis, but most of them usually introduce theoretics and carry out the analysis only presenting the results, such as estimated odds ratio and forest plots. This book not only makes readers aware of why the meta-analysis approaches are derived, but also provides excellent practical skills to synthesize different clinical trials. … I recommend this book as a nice reference for beginners and researchers who are interested in meta-analysis."
—Journal of the American Statistical Association, December 2014
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
Defining the Research Objective
Criteria for Identifying Studies to Include in the Meta-Analysis
Searching For and Collecting the Studies
Data Abstraction and Extraction
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 of Lamotrigine Studies
Meta-Analysis for Continuous Data
Two Published Datasets
Methods for Continuous Data
Meta-Analysis of Tubeless versus Standard Percutaneous Nephrolithotomy
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
Data Analysis Using R
Individual Patient-Level Data Analysis versus Meta-Analysis
Treatment Comparison for Changes in HAMD
Treatment Comparison for Changes in MADRS
Simulation Study on Continuous Outcomes
Meta-Analysis for Rare Events
The Rosiglitazone Meta-Analysis
Step-by-Step Data Analysis in R
Other R Packages for Meta-Analysis
Combining p-Values in Meta-Analysis
R Packages for Meta-Analysis of Correlation Coefficients
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.