Overview

Too often in biostatistical research and clinical trials, a knowledge gap exists between developed statistical methods and the applications of these methods. Filling this gap, Clinical Trial Data Analysis Using R provides a thorough presentation of biostatistical analyses of clinical trial data and shows step by step how to implement the statistical methods using R. The book's practical, detailed approach draws on the authors' 30 years of real-world experience in biostatistical research and clinical development. Each chapter presents examples of clinical trials based on the authors' actual experiences in clinical drug development. Various biostatistical methods for analyzing the data are then identified. The authors develop analysis code step by step using appropriate R packages and functions. This approach enables readers to gain an understanding of the analysis methods and R implementation so that they can use R to analyze their own clinical trial data. With step-by-step illustrations of R implementations, this book shows how to easily use R to simulate and analyze data from a clinical trial. It describes numerous up-to-date statistical methods and offers sound guidance on the processes involved in clinical trials.

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9781439840207

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Reviews

<p>The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job covering most, if not all, major aspects of clinical trial statistics. For those who are well versed in R, this book can serve as a good reference to the established clinical biostatistics methodology; for veteran biostatisticians, this book provides a gentle introduction to the use of R in clinical trial analysis. a great introductory book for clinical biostatistics with an emphasis on R implementations. I would highly recommend it The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods.<br> Journal of Statistical Software, Vol. 43, July 2011

Author Information

Ding-Geng (Din) Chen is the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics and professor of biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University. Dr. Chen's research interests include microarray, genetics, clinical trials, environmental, and toxicological applications as well as biostatistical methodological development in Bayesian models, survival analysis, and statistics in biological assays. Karl E. Peace is the Georgia Cancer Coalition Distinguished Cancer Scholar, founding director of the Center for Biostatistics, and professor of biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University. 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, and recognition by the Georgia and US Houses of Representatives. Drs. Chen and Peace previously collaborated on the book Clinical Trial Methodology (CRC Press, July 2010.)

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