Policy decisions for public health practices often rely on randomized controlled clinical trials. One of the increasingly used designs for randomized controlled clinical trials is non-inferiority (NI). The primary goal of a NI trial is to show that a new therapy is not worse than a standard therapy by an acceptable margin. The choice of a margin is not straightforward, it relies on both historical data, and clinical experts’ opinion. Knowing the true, objective clinical margin would be helpful for design and analysis of NI trials, but it is not possible in practice. We propose to treat NI margin as missing information. In order to recover an objective margin, we believe it is essential to conduct a survey among a group of clinical experts. We introduce a novel framework, where data obtained from a survey are combined with NI trial data, so that both an estimated clinically acceptable margin and its uncertainty are accounted for when claiming NI. Through simulations we compare several methods for implementing this framework. We believe that our approach could provide public health decision makers with more reliable information and thus facilitate a better decision-making process.