Chair: Yuan Ji, University of Chicago

Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author of over 150 publications in peer-reviewed journals including across medical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. His work on cancer genomics has been reported by a large number of media outlets in 2015. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.

Dose-Finding Designs with Late-Onset Toxicities

Speaker: Tianjian Zhou, Colorado State University

Tianjian Zhou is an Assistant Professor in the Department of Statistics at Colorado State University. His research interests include Bayesian methods with applications to clinical trial designs, statistical genomics, missing data, infectious diseases, and veterinary sciences. He earned his Ph.D. in statistics from the University of Texas at Austin in 2017. Prior to joining the faculty at CSU, he held postdoctoral appointments at the University of Chicago and NorthShore University HealthSystem.

Abstract

In oncology dose-finding trials, due to staggered enrollment, it might be desirable to make dose-assignment decisions in real-time in the presence of pending toxicity outcomes, for example, when the dose-limiting toxicity is late-onset. Patients' time-to-event information may be utilized to facilitate such decisions. We review statistical frameworks for time-to-event modeling in dose-finding trials and summarize existing designs into two classes: TITE designs and POD designs. TITE designs are based on inference about toxicity probabilities, while POD designs are based on probabilities of dose-assignment decisions. These two classes of designs contain existing individual designs as special cases and also give rise to new designs.

Randomized Screening Selection Design for Pediatric Oncology Trials

Speaker: Haitao Pan, St. Jude Children’s Research Hospital

Dr. Haitao Pan is an Associate Professor of Biostatistics at St. Jude Children’s Research Hospital. His research primarily centers around the development of innovative clinical trial designs, particularly in the field of oncology, spanning from early to late phases. He Joined St. Jude in 2017 and has authored over 50 publications in peer-reviewed journals, spanning medical and statistical domains, and has published a book through Springer Nature. Dr. Pan has developed 18 R software packages, focusing on adaptive clinical trial design and sample-size calculation. Many of these packages have been instrumental in developing clinical trial protocols at St. Jude. Additionally, he serves as an adjunct faculty member at Florida State University and The University of Memphis. Dr. Pan holds two Ph.D. degrees: one in Preventive Medicine from China, and another in Biostatistics from MD. Anderson Cancer Center.

Abstract

This talk introduces screening selection design (SSD) for randomized pediatric oncology trials with binary and time-to-event endpoints. The absence of a control arm is a characteristic of SSD trials; however, it can facilitate direct comparison between different treatment arms through the selection stage within SSD. From our perspective, SSD trials occupy an intermediate position between separate single-arm trials and multi-arm trials with a control group. SSD should serve as a valuable tool for studying rare diseases or conducting studies with limited sample size available, particularly in the proof-of-concept stage of drug development. We will introduce two St. Jude pediatric oncology trials utilizing the SSD methods: one in pediatric neuroblastoma and the other in pediatric Ewing sarcoma. The chapter will discuss the methodology and implementation of screening selection designs using the R package frequentistSSD.