Statistical Design and Analysis of Clinical Trials

Weichung Joe Shih, PhD, is professor and chair of the Department of Biostatistics in the Rutgers School of Public Health at Rutgers University, and director of the Biometrics Division at the Rutgers Cancer Institute of New Jersey. He is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. He served on the advisory board of the U.S. FDA for reviewing new drug applications and was associate editor of professional journals, including Statistics in Medicine, Controlled Clinical Trials, Clinical Cancer Research, Statistics in Biopharmaceutical Research, and Statistics in Bioscience. His research interests include adaptive designs and missing data issues. Joseph Aisner, MD, is a professor of medicine and a professor of environmental and occupational medicine at the Robert Wood Johnson Medical School of Rutgers University, director of the Medical Oncology Unit at the Robert Wood Johnson University Hospital, and co-leader of the Clinical Investigations Program at the Rutgers Cancer Institute of New Jersey. He is a fellow of the American College of Physicians and the American Society of Clinical Oncology. He serves on and chairs several National Data Monitoring Committees and has served on the editorial board of multiple journals, including Journal of Clinical Oncology, Cancer Therapeutics, Medical Oncology, Clinical Cancer Research, and Hematology-Oncology Today. His research interests include cancer clinical trials and evaluation of therapeutic interventions.

Overview. Concepts and Methods of Statistical Designs. Efficiency with Trade-Offs and Crossover Designs. Sample Size and Power Calculations. Analysis of Covariance and Stratified Analysis. Sequential Designs and Methods—Part I: Expected Sample Size and Two-Stage Phase II Trials in Oncology. Sequential Designs and Methods—Part II: Monitoring Safety and Futility. Sequential Designs and Methods—Part III: Classical Group Sequential Trials. Monitoring the Maximum Information. Missing Data.