TITLE: Clinical Trial Data Analysis Using R & SAS
SPEAKERS: Professor Din Chen, UNC & Pinggao Zhang, PhD, Takeda
MODERATOR: Walter Young
This tutorial is based on the book: “Clinical Trial Data Analysis Using R and SAS” co-authored by (Din) Ding-Geng Chen, Karl E. Peace and Pinggao Zhang, published by Chapman and Hall/CRC Biostatistics Series in 2017, which uses R and SAS to design and analyze clinical trials. This tutorial provides a thorough presentation of biostatistical analyses of clinical trial data with detailed step-by-step illustrations on their implementation using R and SAS. Examples of clinical trials based on the authors’ actual experience in clinical trials in various therapeutic areas are presented. After understanding the application, various biostatistical methods appropriate for analyzing data from the trials are identified. Then statistical programming code is developed using appropriate R/SAS packages to analyze the data. The code development and results are presented in a stepwise fashion. This stepwise approach should enable students to follow the logic and gain an understanding of the analysis methods and the R/SAS implementation so that they may use R/SAS to analyze their own clinical trial data.
Topics To Be Covered:
- Fundamentals of clinical trial design: a brief introduction will be given to design factors including randomization, blinding, bias source and control, endpoint, patient selection, sample size and power.
- Basics of clinical trial data interpretation: a high-level discussion will be given to protocol, statistical analysis plan, topline results, and clinical report.
- Treatment comparisons with continuous/categorical endpoints: We start with simple two treatment comparisons using t-test and extend the analysis to multiple treatment comparisons (analysis of variance) and then to analysis of covariance with clinical covariates.
- Longitudinal clinical trials: We will illustrate longitudinal trials and their analysis using linear mixed models for continuous endpoints, generalized linear mixed model and GEE for categorical endpoints.
- Bayesian analysis in clinical trials using MCMC.
Dr. Din Chen is now the Wallace H. Kuralt distinguished professor in Biostatistics at University of North Carolina-Chapel Hill. Before this, Dr. Chen was a professor in biostatistics at the University of Rochester Medical Center, the Karl E. Peace endowed eminent scholar chair and professor in biostatistics from the Jiann-Ping Hsu College of Public Health at the Georgia Southern University. Dr. Chen is an elected fellow of American Statistical Association (ASA), an elected member of the International Statistics Institute (ISI) and a senior expert consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics. Dr. Chen has more than 200 referred professional publications and co-authored/co-edited 25 books on biostatistics clinical trials, biopharmaceutical statistics, interval-censored survival data analysis, meta-analysis, public health statistics, statistical causal inferences; statistical methods in big-data sciences and Monte-Carlo simulation based statistical modeling. Dr. Chen has been invited nationally and internationally to speak and give short courses at various scientific conferences. In fact, Dr. Chen is a committee member of the Deming Conference and has been invited to give various tutorials at Deming Conference since 2011.
Dr. Pinggao Zhang is now a team lead and director of biostatistics in Takeda Pharmaceutical Company Limited, Cambridge, MA. He previously worked for Shire, Purdue Pharma, Scirex, and Aventis with increasing responsibilities. Dr. Zhang has been managing and leading biostatistics activities in support of clinical research across all development phases, regulatory submissions, and publications. He has worked in various therapeutic areas including vaccine, oncology, CNS, analgesics, immunology, and hematology, and has contributed to several successful drug approvals. In addition, Dr. Zhang is a committee member of the Deming Conference and has served as an invited speaker at various occasions.
Both speakers have extensive experience in biostatistical and clinical trial methodological development and consulting to government and biopharmaceutical industries in SAS and R.