ON-DEMAND WEBINAR · April 8, 2021 - 08:00 AM PDT | 11:00 AM EDT | 17:00 CET | 20:30 IST
Bayesian Adaptive Designs for 21st Century Clinical Trials
- Session Overview: In this webinar the concept of Bayesian statistics and its application in modern clinical trials.
Overview
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.
What You'll Learn
- The key difference between Bayesian statistics and p-value based frequentist statistics.
- 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.
Who Should Attend
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.
Meet the Speaker
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.
Earn CEUs
SCDM is authorized by IACET to offer 0.2 CEUs for this program. 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.
Price
SCDM members: $50
Non-members: $175
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