Bayesian Approaches to Address Clinical Development Challenges for COVID-19 Vaccine/Drugs/Devices
Vaccine, drug and device trials for COVID-19 have created a challenge to design trials within a substantially shortened time frame. There are many complexities involved in selecting an optimal design (primary and secondary endpoints, sample size, interim looks, etc.) without prior information on the intervention, and with limited but rapidly accumulating data on the course of the disease and outcomes.
COVID-19 studies recruit very fast and early interim looks may be very important for clinical practice, allowing reduced patient exposure to futile or harmful health technologies, and rapid re-focus on potentially more promising trials.
Watch this panel, which discusses how adaptive Bayesian designs may help to address the challenges inherent in COVID-19 trials, enabling efficient utilization of available clinical information, including data external to the trial, to optimize sample sizes, carry out early adaptation in design parameters when required, and expedite the accumulation of evidence about treatment effects and harms.
- Natalia Muhlemann, M.D., MBA, Vice President, Strategic Consulting, Cytel (Moderator)
- Rajat Mukherjee, PhD, Research Fellow, Cytel
- Frank Harrell, PhD, Professor of Biostatistics, Vanderbilt University
- Gregory Campbell, PhD, Former Director of Biostatistics, U.S. Food and Drug Administration
- Bernard Fritzell, M.D., Executive Advisor, Cytel