Clinical Trial Data Analysis Using R - Ding-Geng (Din) Chen

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Résumé :
With examples based on the authors' 30 years of real-world experience in many areas of clinical drug development, this book provides a thorough presentation of clinical trial methodology. It presents detailed step-by-step illustrations on the implementation of the open-source software R. Case studies demonstrate how to select the appropriate clinical trial data. The authors introduce the corresponding biostatistical analysis methods, followed by the step-by-step data analysis using R. They also offer the R program for download, along with other essential data, on their website--Provided by publisher.

Biographie:
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.)

Sommaire:
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.

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