Clinical Trial Data Analysis Using R

Ding-Geng (Din) Chen, Karl E. Peace

December 14, 2010 by CRC Press
Reference - 387 Pages - 61 B/W Illustrations
ISBN 9781439840207 - CAT# K11861
Series: Chapman & Hall/CRC Biostatistics Series


Add to Wish List
FREE Standard Shipping!


  • Explains how to select the appropriate statistical method to analyze clinical trial data
  • Applies R to real clinical trial data sets from a hypertension trial, a large duodenal ulcer trial, a large trial of beta blockers, a trial of familial andenomatous polyposis, and a Phase II breast cancer trial
  • Offers a basic introduction to R, including how to install R and upgrade R packages
  • Covers the biostatistical aspects of various clinical trials, including treatment comparisons, those with time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials
  • Explores Bayesian modeling and Markov chain Monte Carlo simulations
  • Analyzes microarray data derived from samples collected in clinical trials using the Bioconductor project


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.