The 3rd Stat4Onc Annual Symposium
Precision oncology trials
Speaker Biography and Abstract
Daniel Catenacci, MD
Peter F. Thall, Ph.D.
Department of Biostatistics
M.D. Anderson Cancer Center
Peter F. Thall is the Anise J. Sorrell Professor in the Department of Biostatistics at M.D. Anderson Cancer Center, and an adjunct professor in the Department of Statistics at Rice University. He is a Fellow of the American Statistical Association and the Society for Clinical Trials, and received the Don Owen Award in 2014. Dr. Thall has published over 250 papers and book chapters in the statistical and medical literature, and co-authored the 2016 book Bayesian Designs for Phase I-II Clinical Trials. His latest book, Statistical Remedies for Medical Researchers, is in press and should become available in the summer of 2019. Dr. Thall’s research areas include clinical trial design, precision medicine, Bayesian nonparametric statistics, incorporating expert opinion into Bayesian inference, and dynamic treatment regimes. He has presented over 200 invited talks and 30 short courses, served as an associate editor for Journal of the National Cancer Institute, Statistics in Medicine, Statistics in Biosciences, Clinical Trials, and Biometrics, and is an ASA media expert.
Bayesian Oncology Clinical Trial Designs with Subgroup-Specific Decisions
This talk will present Bayesian utility-based designs for two cancer clinical trials that make subgroup-specific outcome-adaptive decisions. The first is a randomized trial comparing nutritional prehabilitation to standard of care for controlling post-operative morbidity (POM) after chemoradiation and esophageal resection. The design uses elicited utilities of POM scored as a five-level ordinal variable, accounts for two prognostic subgroups, and assumes a robust model for POM as a function of treatment and subgroup. The second is an early phase trial to optimize the dose of umbilical cord blood derived natural killer cells for treating advanced hematologic malignancies. The design does sequential dose-finding and safety monitoring within each of six prognostic subgroups, with decisions based on joint utilities of five co-primary time-to-event outcomes monitored over 100 days post cell infusion. Simulation studies establishing each design’s operating characteristics are presented.
Ying Lu, Ph.D.
Professor of Biomedical Data Science
Co-Director, Biostatistics Core of Stanford Cancer Institute (NIH Comprehensive Cancer Center)
Co-Director, Center for Innovative Study Design
Stanford University School of Medicine
Dr. Lu received his Ph.D. in Biostatistics from the University of California, Berkeley and was faculty in the University of Miami School of Medicine and the University of California, San Francisco. He also served as the Director of VA Cooperative Studies Palo Alto Coordinating Center from 2009-2016. His research focuses include clinical trial design, early phase cancer trials, radiology, medical diagnosis and prognostic prediction, medical decision making, and statistical applications. He serves as biostatistical editor of the JCO Precision Oncology, and is an ASA elected fellow, ICSA 2014 President, and President Elect of WNAR 2019.