May 6, 2021
8:15 AM – 12 :15 PM Pacific Time

Innovative Designs for Early Phase Oncology Trials

Yuan Ji, The University of Chicago

Sue-Jane Wang, US FDA

  In this half-day short course, we will introduce, describe, and demonstrate innovative designs for early-phase oncology trials. The classroom teaching will be delivered by the two instructors through projected slides. There will be two sessions with a Q&A portion in each session. One session will be taught by Dr. Sue-Jane Wang. She will recap types of methodological advances in light of recent regulatory experiences with use of early phase innovative designs. The other session will be taught by Dr. Yuan Ji, who will present novel statistical designs for phase 1a dose escalation, phase 1b expansion cohorts, drug combination dose finding, designs accommodating delayed toxicity like in immune oncology, and designs for master protocols. Most designs introduced in the short course will use Bayesian modeling and adaptive decision rules. A brief introduction of Bayesian statistics will also be provided. Lastly, at the end two software packages will be demonstrated, one of which is freely available and the other commercial. 

  Outline of Topics:
Session 1 (2 hours, Ji): Methodologies and applications
  1. Designs for early-phase dose escalation studies
  2. Designs for early-phase cohort expansion studies
  3. Designs for early-phase drug combination dose finding studies
  4. Designs for immune oncology dose finding with delayed outcomes
  5. Designs and methods for exploratory basket trials
Q&A and break (30 minutes)
Session 2 (1 hour, Wang): Regulatory consideration and rationales
  1. Regulatory guidance overview related to early phase dose finding/cohort expansion/adaptive design/master protocol/new drug combinations
  2. Key methodologies recap and some regulatory experiences in early phase design strategies

Session 3 (1 hour, Ji): Software demo.


Yuan Ji

The University of Chicago

james Dr. Yuan Ji graduated from Fudan University with a bachelor in Mathematics, University of Wisconsin – Madison with a PhD in Statistics. He spent 9 years at The University of Texas M. D. Anderson Cancer Center as Assistant and Associate Professor in Biostatistics and Bioinformatics. Currently, Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. He is an NIH-funded PI focusing on innovative computational and statistical methods for translational cancer research. Dr. Ji is author of over 150 publications in peer-reviewed journals, conference papers, book chapters, and abstracts. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and mTPI-2 designs, which have been widely applied in dose-finding clinical trials. His recent work on precision medicine was elected as one of the top 10 ideas of the Precision Trials Challenge hosted by The Harvard Business School in 2015. Dr. Ji is an elected fellow of ASA.

Sue-Jane Wang


james Dr. Sue-Jane Wang is an Associate Director, Office of Biostatistics (OB) in the Office of Translational Sciences, CDER, US FDA. She is also the OB Biostatistics Liaison to Office of New Drugs for the FDA/CDER Biomarker Qualification Program. Dr. Wang has joined FDA for more than 20 years. Currently, she is also helping the Biometrics Division that provides regulatory statistical services to cardio- renal, neurology, psychiatry and medical imaging drug product developments. In her roles, Dr. Wang has been contributing to FDA guidance developments as an Office Lead or a guidance working group member including, e.g., pre-market evaluation on clinical pharmacogenomics, adaptive design, enrichment strategies, drug development tools, co-development of an In Vitro companion diagnostic device with a therapeutic product, multiple endpoints, analytical validation of biomarker for qualification. She is a member of FDA- NIH Biomarker Working Group. Dr. Wang has been active in complex trial design, pharmacogenomics, biomarker, diagnostic imaging, theranostics research and professional editorship. She has been awarded for her professional recognitions including, e.g., a Fellow of the American Statistical Association, an FDA Level Scientific Achievement (Individual) Award on Excellence in Analytical Science.