Making the Most of Your Data II: Optimizing Clinical Information in Trial Design and Implementation Using Bayesian Methods
While there is increasing optimism about the discovery of a COVID-19 vaccine, one of the less talked about aspects of such vaccines development are the lessons that can be used in other therapeutic areas. After all, COVID-19 vaccines development has uncovered numerous ways to design and execute trials within shorter time-frames and with less data.
One theme that has emerged consistently is the need to optimize the use of clinical information available, an endeavor well-supported by Bayesian methods.
Clinical information in this case is observational data gleaned from actual medical practice, which can then be incorporated into randomized clinical trials.
This Whitepaper on Bayesian Methods for COVID-19 Vaccines Development considers issues like:
Ensuring higher confidence in early decision-making;
Adapting to high enrolment rates and higher speed of development;
Planning phase 2 trials that enable optimum endpoint selection in Phase 3;
How to choose statistical methods that optimize clinical objectives;
The overall flexibility of Bayesian methods.
Clearly the benefits of these strategies are far more widely applicable than in COVID-19 drug discovery. Cytel scientists and statisticians have been working to develop COVID Vaccines Trials across the globe, and have shown that strategies for maximizing the use of clinical information often benefit from Bayesian methods. These methods though are very critical to the broader objective of optimizing the use of clinical information more generally.