Keywords: Clustered randomized trial, Experimental design, Non-bipartite matching, Optimal pair-matching, Propensity score matching, Randomization tests, Repeated randomization
Minimization, a dynamic allocation method, is gaining popularity especially in cancer clinical trials. Aiming to achieve balance on all important prognostic factors simultaneously, this procedure can lead to a substantial reduction in covariate imbalance compared with conventional randomization in small clinical trials. While minimization has generated enthusiasm, some controversy exists over the proper analysis of such a trial. Critics argue that standard testing methods that do not account for the dynamic allocation algorithm can lead to invalid statistical inference. Acknowledging this limitation, the International Conference on Harmonization E9 guideline suggests that ‘the complexity of the logistics and potential impact on analyses be carefully evaluated when considering dynamic allocation’. In this article, we investigate the proper analysis approaches to inference in a minimization design for both continuous and time-to-event endpoints and evaluate the validity and power of these approaches under a variety of scenarios both theoretically and empirically.