Statistical Design, Monitoring, and Analysis of Clinical Trials

Statistical Design, Monitoring, and Analysis of Clinical Trials, Second Edition concentrates on the biostatistics component of clinical trials. This new edition is updated throughout and includes five new chapters.

Developed from the authors’ courses taught to public health and medical students, residents, and fellows during the past 20 years, the text shows how biostatistics in clinical trials is an integration of many fundamental scientific principles and statistical methods. The book begins with ethical and safety principles, core trial design concepts, the principles and methods of sample size and power calculation, and analysis of covariance and stratified analysis. It then focuses on sequential designs and methods for two-stage Phase II cancer trials to Phase III group sequential trials, covering monitoring safety, futility, and efficacy. The authors also discuss the development of sample size reestimation and adaptive group sequential procedures, phase 2/3 seamless design and trials with predictive biomarkers, exploit multiple testing procedures, and explain the concept of estimand, intercurrent events, and different missing data processes, and describe how to analyze incomplete data by proper multiple imputations.

This text reflects the academic research, commercial development, and public health aspects of clinical trials. It gives students and practitioners a multidisciplinary understanding of the concepts and techniques involved in designing, monitoring, and analyzing various types of trials. The book’s balanced set of homework assignments and in-class exercises are appropriate for students and researchers in (bio)statistics, epidemiology, medicine, pharmacy, and public health.

Weichung Joe Shih, PhD, has been tenured professor and chair of the Department of Biostatistics, Rutgers School of Public Health, Rutgers University, New Brunswick, New Jersey, and director of Biometrics Division at the Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey. He is an elected fellow of the American Statistical Association (1996) and an elected member of the International Statistical Institute (2001). Prior to joining academia, he spent his formative years (1982-1999) at Merck Research Laboratories, Rahway, New Jersey. He served in the Advisory Board of the US 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. He pioneered in the field of sample size re-estimation for clinical trials, which has evolved into the field of adaptive designs. He also first advocated the use of consistency criterion for international bridging studies, which is now adopted by the ICH guidance for global multiregional clinical trials. He has collaborated extensively with physicians in various therapeutic areas and authored numerous papers in statistical methodology in clinical trials. His research interests include adaptive designs and missing data issues. He has been honored as professor emeritus of Rutgers University since July 2019.

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, New Brunswick, New Jersey, director of Medical Oncology Unit at the Robert Wood Johnson University Hospital, New Brunswick, New Jersey, and co-leader of the Clinical Investigations Program at the Rutgers Cancer Institute of New Jersey. He has published extensively 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. 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, has served on multiple National Institutes of Health (NIH) Study Sections, and has headed two National Cooperative Cancer Study Groups. His research interests include cancer clinical trials and evaluation of therapeutic interventions.