Abstract:
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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 to 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 ICH 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.
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