To maintain the interpretability of the effect of experimental treatment obtained from a noninferiority trial, current statistical approaches often require the constancy assumption. This assumption typically requires that the control treatment effect in the population of the active control trial is the same as its effect presented in the population of the historical trial. To prevent constancy assumption violation, the clinical trials sponsors were recommended to make sure the design of the active control trial is as close to the design of the historical trial as possible. However, these rigorous requirements were rarely fulfilled in practice. In this talk, we present a covariate-adjustment approach to achieve two goals: 1) to quantify the impact of population difference between the historical trial and the active control trial to the degree of constancy assumption violation; 2) to redefine the active control treatment effect in the active control trial population if the quantification suggests an unacceptable violation.