The approval and differentiation of new compounds in clinical development often demands non-inferiority trials, in which the test drug is compared against a reference treatment. However, non-inferiority trials impose major operational burden with serious ethical and scientific implications for the development of new medicines. Traditional approaches make limited use of historical information on placebo and neglect inter-trial variability, relying on the constancy assumption that the control-to-placebo effect size is maintained across trials. We propose a model-based approach that overcomes such limitations and may be used as a tool to explore differentiation during clinical development. Parameter distributions are introduced which reflect the heterogeneity of trials. The method is illustrated using data from impetigo trials. Based on simulation scenarios, this Bayesian technique yields a definitive, consistent increase in the statistical power over two accepted statistical methods, allowing lower sample size requirements for the assessment of non-inferiority.