Applied Meta-Analysis with R
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Applied Meta-Analysis with R

Applied Meta-Analysis with R


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About the Book

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 extensiv

Table of Contents:
Introduction to R. Research Protocol for Meta-Analyses. Fixed Effects and Random Effects in Meta-Analysis. Meta-Analysis with Binary Data. Meta-Analysis for Continuous Data. Heterogeneity in Meta-Analysis. Meta-Regression. Individual Patient-Level Data Analysis versus Meta-Analysis. Meta-Analysis for Rare Events. Other R Packages for Meta-Analysis. Index.

About the Author :
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.

Review :
"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


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Product Details
  • ISBN-13: 9781466506008
  • Publisher: Taylor & Francis Inc
  • Publisher Imprint: Chapman & Hall/CRC
  • Language: English
  • No of Pages: 342
  • ISBN-10: 1466506008
  • Publisher Date: 03 May 2013
  • Binding: Digital (delivered electronically)
  • No of Pages: 342


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