Podcast Explains New Bayesian Methods Trial Guidance

A new CDER guidance recap podcast explains the newly published FDA draft guidance on the use of Bayesian methodology in clinical trials of drug and biological products. On the podcast, CDER Office of Biostatistics master mathematical statistician James Travis says the guidance is important because, as the number of proposals for trials that use Bayesian approaches increases, it is important to ensure that FDA’s needs and expectations are clear for sponsors as they propose and implement such approaches.

Travis summarizes the draft guidance recommendations, saying that drug trial sponsors can use Bayesian methods in various ways, including to govern the timing and adaptation rules for an interim analysis in an adaptive design; to inform design elements, such as dose selection, for subsequent clinical trials, or as the key analysis to support primary inference in a trial.

The draft addresses relevant considerations on how to design clinical trials using Bayesian approaches so that the trial meets the regulatory requirements for drug and biological product development programs, he says. It also discusses important operating characteristics that are measures of how a trial is likely to perform and succeed and are key to informing factors such as sample size.

Finally, Travis says, the guidance has recommendations on the use of software for Bayesian inference, considerations for missing data when using previous data to construct the prior, and detailed recommendations on how to document Bayesian approaches when designing and planning a study and reporting the results following the completion of the study. It also has examples from drug development programs where Bayesian methods were used.

Asked what he particularly wants podcast listeners to remember, Travis highlighted:

• the guidance has recommendations to facilitate the appropriate use of Bayesian statistical methods in making primary inference from clinical trials that evaluate new drug safety and effectiveness;
• in a Bayesian analysis, data collected in a study are combined with a prior distribution that captures the pre-study information about a parameter of interest to form a posterior distribution that expresses the updated, post-study information about the parameter of interest;
• in general, the process for determining a prior should begin with an identification and review of all relevant external information that is available;
• the guidance has examples of how drug development programs have used Bayesian approaches in clinical trials that test the safety and effectiveness of a new drug; and
• sponsors should discuss with FDA their plans for using Bayesian methods as early as possible and before beginning their study.