Keywords: bayesian, prior elicitation, expert judgment
As the use of Bayesian approaches continues to expand in the field of drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the evidential basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data-driven prior. Even in cases where a substantial body of empirical evidence is available, there is very often a translational gap between the setting(s) to which the available data relate and the new setting under consideration. In such cases, how does one leverage the wealth of expert knowledge and experience within the pharmaceutical organization and/or biomedical community to develop robust informative priors that can bridge the gap between sparse or indirect evidence and the quantities of interest to decision makers?