Applied Meta-Analysis with R

Applied Meta-Analysis with R

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Features

  • Represents one of the first books on how to use R for analyzing meta-data
  • Provides up-to-date meta-analysis methods and models and illustrates their application to biomedical research
  • Describes a variety of real clinical trials with the associated clinical data
  • Offers easy access to computational methods using R functionality and packages, such as meta, rmeta, and metafor
  • Gives step-by-step presentations of the code development and results
  • Requires no prior experience with R

Summary

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.

Table of Contents

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

Author Bio(s)

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.

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