Clinical Trial Data Analysis Using R and SAS (2^nd Ed.) Chapman & Hall/CRC Biostatistics Series

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.

Preface. Introduction to R. Overview of Clinical Trials. Sample Size Determination in Clinical Trials. Two Treatment Comparisons in Clinical Trials. Multi-Arm Comparisons in Clinical Trials (ANOVA). Treatment Comparisons Incorporating Covariates in Clinical Trials (ANCOVA). Clinical Trials with Time-to-Events Endpoints. Clinical Trials with Repeated Measures. Meta-Analysis in Clinical Trials. Bayesian Methods in Clinical Trials. Group Sequential Designs and Monitoring in Clinical Trials. Bioequivalence Clinical Trials. Monitoring Clinical Trials for Adverse Events.

Ding-Geng (Din) Chen, Ph.D., is a professor at the University of Rochester Medical Center. Dr. Chen has vast experience in

biostatistical research and clinical trial development and methodology. He has authored or co-authored more than 100 journal

publications on biostatistical methodologies and applications. He is also the co-author (with Dr. Peace) of Clinical Trial Methodology

and Clinical Trial Data Analysis Using R and a co-editor (with Drs. Sun and Peace) of Interval-Censored Time-to-Event Data: Methods

and Applications. He is a member of the American Statistical Association, chair for the STAT section of the American Public Health

Association, an associate editor of the Journal of Statistical Computation and Simulation, and an editorial board member of several

other journals.

Karl E. Peace, Ph.D., is the Georgia Cancer Coalition Distinguished Cancer Scholar, senior research scientist, and professor of

biostatistics in the Jiann-Ping Hsu College of Public Health at Georgia Southern University. He is also an adjunct professor of

biostatistics at the VCU School of Medicine. Dr. Peace is a reviewer or editor of several journals, the founding editor of the Journal of

Biopharmaceutical Statistics, and a fellow of the American Statistical Association. He has authored or co-authored over 150 articles

and 10 books. He has received numerous awards, including the University System of Georgia Board of Regents’ Alumni Hall of Fame

Award, the First President’s Medal for outstanding contributions to Georgia Southern University, and distinguished meritorious service

awards from the American Public Health Association and other organizations. In 2012, the American Statistical Association created the

Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society.