Why You Should Construct Primary Endpoints Using Bayesian Methods: Lessons From COVID-19

One of the revelations of the COVID-19 pandemic is that the flexibility and potential of Bayesian designs goes far beyond the benefits connected to informed priors. Rather a number of other benefits to Bayesian designs are sometimes under-appreciated. The importance of using Bayesian methods to choose composite endpoints, for example, particularly in longitudinal studies, can be overlooked when considering Bayesian and Frequentist options.

Cytel statisticians reflected on these benefits during a recent panel discussion.

During a recent panel on COVID-19 drug discovery, led by Cytel VP of Strategic Consulting Natalia Muhlemann, biostatisticians and former regulators reflected on certain misconceptions about Bayesian methods. First and foremost was the fact that the primary advantage of Bayesian designs is that they cut short trial timelines by employing informed priors, that is information about a new therapy gleaned from previous trials and related data.