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Bayesian Adaptive Designs for 21st century Clinical Trials

Bayesian Adaptive Designs for 21st Century Clinical Trials

Date: April 8, 2021
Time: 08:00 AM PDT | 11:00 AM EDT | 17:00 CET | 20:30 IST


In this webinar the concept of Bayesian statistics and its application in modern clinical trials. In particular, the webinar will show the key differences between Bayesian designs, modelling, and analysis and p-value based traditional fixed designs. Examples, include COVID-19 vaccine trials, will be provided focusing on Bayesian adaptive dose-finding designs. The webinar will illustrate how adaptive decisions will need to made throughout the course of the trial. An important feature of Bayesian adaptive designs is the flexibility in making adaptive decisions based on trial interim data. This requires a close collaboration with data management team for the trial to streamline the data preparation, statistical analysis, and decision making. Software tools that allow such decision making is important. A commercial software package will be demonstrated.


  • The key difference between Bayesian statistics and p-value based frequentist
  • The features of Bayesian modeling and decision making for clinical trials.
  • The data management requirements to implement Bayesian designs in real world.
  • Examples of Bayesian adaptive designs.


Audience should have basic concept of clinical trial designs and decision making
based on data and evidence. Beginners for statistics are fine as the talk will not
be technical.


The participants should learn the basic philosophy of Bayesian statistics, why it is
not the main-stream method and why it will become more and more popular for future
clinical trials. Also, relevance to data management that supports Bayesian adaptive
design should become clear after this webinar.


Yuan Ji, PhD, Professor Biostatistics, 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, conference papers, book chapters, and abstracts, including Nature, Nature Methods, JCO, JNCI, JASA, and Biometrics, 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, including trials published on Lancet Oncology, JAMA oncology and JCO. His work on cancer genomics has been reported by a large number of media outlets in 2015. In particular, he led a publication in Nature Methods and invention of a tool called TCGA-Assembler which has been downloaded over 10,000 times worldwide. 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. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.


Participants are eligible to receive CEUs upon attendance and successful completion of a web-based assessment within 30 days after the webinar. CEUs are not granted after the 30-day assessment deadline.

SCDM is authorized by IACET to offer 0.2 CEUs for this program.


Registration Member Non-member
Individual $360 $420
Group (max. 10 ppl.) $960 $1,140

Group Registration Policy:
Any group registration allows up to 10 people to attend the webinar and to complete the webinar assessment. Credits will be granted on an individual basis upon successful completion of the assessment by the registered participants.

Refund Policy: Participants will receive a full refund if notice is provided in writing via post or email one week prior to the webinar date. If cancellation occurs within the week prior the webinar, participants will be allowed to apply 50% of the webinar fee to the next offering of the same webinar. No refunds will be offered after that time.

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