Clinical Trial Data Analysis Using R

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.)

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
Introduction
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


Bibliography


Index


Concluding Remarks appear at the end of each chapter.