Clinical Trial Data Analysis Using R and SAS

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

• Publisher: Chapman and Hall/CRC
• Year: 2017
• Print ISBN: 9780367736217
• eBook ISBN: 9781351651141
• Topic: Mathematics
• Subtopic: Biostatistics
• Index: Mathematics

Review of the First Edition

"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. 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

Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book's practical, detailed approach draws on the authors' 30 years' experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data.

What's New in the Second Edition

• Adds SAS programs along with the R programs for clinical trial data analysis.
• Updates all the statistical analysis with updated R packages.
• Includes correlated data analysis with multivariate analysis of variance.
• Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials.
• Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.

Chapter 1

Introduction to R

In this chapter, we provide a basic introduction to the R system (R Development Core Team (2005)): where to get R, how to install R and upgrade R packages. We also show how easy it is to use R to simulate and analyze data from a simple clinical trial. We conclude the chapter with a brief summary and some recommendations for further reading and references. Readers who already know and have familiarity with R can skip this chapter and go directly to any of the remaining chapters.

1.1 What is R?

To obtain an introduction to R, go to the official homepage of the R project at

http://www.R-project.org

and click “About R” under “R Project”:

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

According to a NY Times article, R first appeared in 1996, when statistics professors Ross Ihaka and Robert Gentleman of the University of Auckland in New Zealand released the code as a free software package (http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html?pagewanted=all&_r=0). Based on a recent IEEE spectrum publication, R currently occupies 5th place among top programming languages (http://blog.revolutionanalytics.com/popularity/).

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

To some users, “free” software may be a “negative” word for software that is difficult to use, has lower quality or utilizes procedures that have not been validated or verified, etc. However, to other users, “free” software means software from an open source that not only allows use of the software, but also permits modifications to handle a variety of applications. This latter description is the fundamental principle for R system.

We now proceed to the steps for installing and using R.

1.2 Steps on Installing R and Updating R Packages

In general, the R system consists of two parts. One is the so-called R base system for the core R language and associated fundamental libraries. The other consists of user contributed packages that are more specialized applications. Both the base system and the packages may be obtained from the Comprehensive R Archive Network (CRAN) from the weblink:

http://CRAN.r-project.org

Installation of R system is described in the following sections.

1.2.1 First Step: Install R Base System

The base system can be downloaded from

http://CRAN.r-project.org

for different platforms of “Linux”, “MacOS X” and “Windows”. In this book, we illustrate the use of R for “Windows”. “Windows” users can download the latest verison of R using the link:

http://CRAN.r-project.org/bin/windows/base/release.htm

(At the writing of this book, version R 3.3.1 is available.) To download and install R to your computer, simply follow the instructions from the installer to install R to the “Program Files” subdirectory in your C. You are ready to use R for statistical computing and data analysis.

Note to LATEX and R/Sweave users: LATEX will complain about the extra space in the path as in “Program Files”. Therefore if you want to use R along with LATEX, you need to make a subdirectory without space in the path to install R.

You should now have an icon with a shortcut to R. Simply click the icon to start R. You should see some introductory information about R and a command prompt ‘>’:

 > 

To illustrate R computation, suppose we wish to calculate the sum of 1 and 2. The first line of R computation is:

 > x = 1+2 

The computed value may be printed using:

 > print(x)

[1] 3 

You should get “3”.

1.2.2 Second Step: Installing and Updating R Packages

The R base system contains a variety of standard statistical functions, descriptive and inferential statistical analysis methods, and graphics which are appropriate for many statistical computing and data analysis requirements.

However, the packages are more specialized applications that are contributed by advanced R developers and users who are experts in their field. From our view, packages in R is the most important component in R development and upgrading. At the time of writing this second edition of our book, there are more than 9203 packages in the R system spanning almost all fields of statistical computing and methodology.

You may install any packages from the R prompt by clicking install.packages from the R menu Packages.

For example, for researchers and practitioners who are interested in designing group sequential clinical trials, the gsDesign contributed by Keaven Anderson from Merck and Company can be installed from this pull-down manual. All the functionality of this package is then available by loading it to R as:

 > library(gsDesign) 

For first-time users for this package, information about its use may be obtained by invoking:

 > library(help=gsDesign) 

A help page is then available which explains all the functionality of this package. For readers who desire a comprehensive list of available packages, go to

http://CRAN.R-project.org/src/contrib/PACKAGES.html

1.2.3 Steps to Get Help and Documentation

A striking feature of R is the easy access of its “Help and Documentation” which may distinguish it from other software systems. There are several ways to access “Help and Documentation”.

The general reference may be obtained from RGui in R. When R is started, click “Help” to access R help items on “FAQ on R”, “FAQ on R on Windows”, “Manuals (in PDF)”, etc. We recommend that readers print the online PDF manual “Introduction to R” for future reference.

Additional “Help and Documentation” may be obtained from the R home-page. Many documentations and online discussions on R are available from the R homepage http://www.r-project.org/. The online “Documentation” section consists of almost a...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Contents
• List of Figures
• List of Tables
• Preface for the Second Edition
• Preface for the First Edition
• About the Authors
• 1 Introduction to R
• 2 Overview of Clinical Trials
• 3 Treatment Comparisons in Clinical Trials
• 4 Treatment Comparisons in Clinical Trials with Covariates
• 5 Analysis of Clinical Trials with Time-to-Event Endpoints
• 6 Longitudinal Data Analysis for Clinical Trials
• 7 Sample Size Determination and Power Calculations in Clinical Trials
• 8 Meta-Analysis of Clinical Trials
• 9 Bayesian Methods in Clinical Trials
• 10 Bioequivalence Clinical Trials
• 11 Adverse Events in Clinical Trials
• 12 Analysis of DNA Microarrays in Clinical Trials
• Bibliography
• Index