Clinical Trial Data Analysis Using R

Clinical Trial Data Analysis Using R

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ISBN 9781439840207
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  • 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.

Table of Contents

Introduction to R
What Is R?
Steps on Installing R and Updating R Packages
R for Clinical Trials
A Simple Simulated Clinical Trial
Concluding Remarks

Overview of Clinical Trials
Phases of Clinical Trials and Objectives
The Clinical Development Plan
Biostatistical Aspects of a Protocol

Treatment Comparisons in Clinical Trials
Data from Clinical Trials
Statistical Models for Treatment Comparisons
Data Analysis in R

Treatment Comparisons in Clinical Trials with Covariates
Data from Clinical Trials
Statistical Models Incorporating Covariates
Data Analysis in R

Analysis of Clinical Trials with Time-to-Event Endpoints
Clinical Trials with Time-to-Event Data
Statistical Models
Statistical Methods for Right-Censored Data
Statistical Methods for Interval-Censored Data
Step-by-Step Implementations in R

Analysis of Data from Longitudinal Clinical Trials
Clinical Trials
Statistical Models
Analysis of Data from Longitudinal Clinical Trials

Sample Size Determination and Power Calculation in Clinical Trials
Prerequisites for Sample Size Determination
Comparison of Two Treatment Groups with Continuous Endpoints
Two Binomial Proportions
Time-to-Event Endpoint
Design of Group Sequential Trials
Longitudinal Trials
Relative Changes and Coefficient of Variation: An Extra

Meta-Analysis of Clinical Trials
Data from Clinical Trials
Statistical Models for Meta-Analysis
Meta-Analysis of Data in R

Bayesian Analysis Methods in Clinical Trials
Bayesian Models
R Packages in Bayesian Modeling
MCMC Simulations
Bayesian Data Analysis

Analysis of Bioequivalence Clinical Trials
Data from Bioequivalence Clinical Trials
Bioequivalence Clinical Trial Endpoints
Statistical Methods to Analyze Bioequivalence
Step-by-Step Implementation in R

Analysis of Adverse Events in Clinical Trials
Adverse Event Data from a Clinical Trial
Statistical Methods
Step-by-Step Implementation in R

Analysis of DNA Microarrays in Clinical Trials
DNA Microarray
Breast Cancer Data



Concluding Remarks appear at the end of each chapter.

Author Bio(s)

Editorial Reviews

"The book is well written and R code is clearly commented in most cases. The chapters provide very useful material for practical clinical trial data analyses. … The results of the analyses are illustrated with excellent graphical presentations. Bootstrap and simulations are utilized extensively."
International Statistical Review, 2013

"The book fills its purpose well, successfully covering the majority of the major trial design and analysis techniques. … a good primer to the most commonly used methods and their utilisation in R, as well as extensive lists of references to more detailed considerations. … this book can certainly stand on any biostatisticians’ shelf as a useful text. For the seasoned methodologist, it provides a helpful introduction to the R environment. For R proponents, it delivers a simple overview of the established clinical biostatistics methodology."
—Michael Grayling, ISCB News, December 2013

"… the book is very well written and includes up-to-date, comprehensive, and carefully selected topics for the analysis of clinical trials and their step-by-step implementation in R."
—Mizanur Khondoker, Statistical Methods in Medical Research, 22(6), 2013

"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."
Journal of Statistical Software, Vol. 43, July 2011

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