OBJECTIVES: When evaluating multiple drugs for equivalence (or non-inferiority) within the context of a Bayesian MTC, most studies base their interpretation solely on the point estimates and respective credible intervals. The following novel methodology advances interpretation by: Incorporating a pre-specified minimal clinically important difference (MCID); presenting a direct probability of equivalence (or non-inferiority), and graphically depicting how the probability varies by MCID. METHODS: As an illustrative example, we applied MTC to compare 12-week HbA1c reduction with vildagliptin 50 mg bid vs. sitagliptin 100 mg qd as monotherapies in patients with type 2 diabetes.  Equivalence was assessed with a predefined equivalence margin of MCID. A Bayesian approach has the advantage of being able to provide probability statements for equivalence, to make direct inferential statement that the treatment effect between the two comparisons is between the specified lower and upper MCID (HbA1c  ±0.7). The posterior probability of equivalence is calculated based on the area under the curve between lower and upper MCID on distribution of the mean change in HbA1c between the two comparisons. Sensitivity analysis was conducted by varying MCID values. RESULTS: The results of the MTC showed no significance difference between the two interventions in the reduction of HbA1c at 12 weeks (Δ = 0.16; 95% CrI -0.20 to 0.52). However, this evidence of “no significant difference” does not prove equivalence.  Applying the new method, at 12 weeks follow-up, the probability that vildagliptin 50 mg bid and sitagliptin 100 mg qd are equivalent is 99.3%. The result of a sensitivity analysis showed that the probability of the two drugs remaining equivalent remains high (>90%) over a wide range of MCIDs. CONCLUSIONS: This innovative method has the potential to improve understanding of equivalence (or non-inferiority) between drugs for multiple stake-holders.