Minimization design, a dynamic allocation method, is gaining popularity in cancer clinical trials. Aiming to achieve balance on all important prognostic factors simultaneously, this procedure can lead to substantial reduction in covariate imbalance in small clinical trials compared to conventional randomization. While this method generates much excitement, some controversy exists over the proper analysis of such a trial. Traditional Gaussian distribution-based analysis method without considering the dynamic allocation mechanism can lead to invalid inference. Acknowledging this limitation, the ICH E9 Guideline suggests that “The complexity of the logistics and potential impact on analyses be carefully evaluated when considering dynamic allocation”. In this talk, we investigate the proper analysis approaches to inference in a minimization design and evaluate the properties of these approaches under a variety of scenarios with simulation studies.