Statistical comparison of random allocation methods in cancer clinical trials

Abstract

The selection of a trial design is an important issue in the planning of clinical trials. One of the most important considerations in trial design is the method of treatment allocation and appropriate analysis plan corresponding to the design. In this article, we conducted computer simulations using the actual data from 2158 rectal cancer patients enrolled in the surgery-alone group from seven randomized controlled trials in Japan to compare the performance of allocation methods, simple randomization, stratified randomization and minimization in relatively small-scale trials (total number of two groups are 50, 100, 150 or 200 patients). The degree of imbalance in prognostic factors between groups was evaluated by changing the allocation probability of minimization from 1.00 to 0.70 by 0.05. The simulation demonstrated that minimization provides the best performance to ensure balance in the number of patients between groups and prognostic factors. Moreover, to achieve the 1 percentile for the p-value of chi-square test around 0.50 with respect to balance in prognostic factors, the allocation probability of minimization was required to be set to 0.95 for 50, 0.80 for 100, 0.75 for 150 and 0.70 for 200 patients. When the sample size was larger, sufficient balance could be achieved even if reducing allocation probability. The simulation using actual data demonstrated that unadjusted tests for the allocation factors resulted in conservative type I errors when dynamic allocation, such as minimization, was used. In contrast, adjusted tests for allocation factors as covariates improved type I errors closer to the nominal significance level and they provided slightly higher power. In conclusion, both the statistical and clinical validity of minimization was demonstrated in our study.