Statistics in Clinical Cancer Research Worth Adapting? Revisiting the Usefulness of Outcome-Adaptive Randomization

Outcome-adaptive randomization allocates more patients to the better treatments as the information accumulates in the trial. Is it worth it to apply outcome-adaptive randomization in clinical trials? Different views permeate the medical and statistical communities. We provide additional insights to the question by conducting extensive simulation studies. Trials are designed to maintain the type I error rate, achieve a specified power, andprovide better treatment to patients. Generally speaking, equal randomization requires a smaller sample size and yields a smaller number of nonresponders than adaptive randomization by controlling type I and type II errors. Conversely, adaptive randomization produces a higher overall response rate than equal randomizationwith orwithout expanding the trial to the samemaximumsample size.When there are substantial treatment differences, adaptive randomization can yield a higher overall response rate as well as a lower average sample size and a smaller number of nonresponders. Similar results are found for the survival endpoint. The differences between adaptive randomization and equal randomization quickly diminishwith early stoppingof a trial due to efficacyor futility. In summary, equal randomizationmaintains balanced allocation throughout the trial and reaches the specified statistical power with a smaller number of patients in the trial. If the trial’s results are positive, equal randomizationmay lead to early approval of the treatment. Adaptive randomization focuses on treating patients best in the trial. Adaptive randomization may be preferred when the difference in efficacy between treatments is large or when the number of patients available is limited. Clin Cancer Res; 18(17); 4498–507. 2012 AACR.