Worth adapting? Revisiting the usefulness of outcome-adaptive randomization

Abstract

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, and provide 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 randomization with or without expanding the trial to the same maximum sample 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 diminish with early stopping of a trial due to efficacy or futility. In summary, equal randomization maintains 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 randomization may 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.

Figure 1

Randomization probability to the experimental treatment versus the number of patients accrued for two-arm trials with binary endpoints. The result of adaptive randomization (AR) and equal randomization (ER) are compared. For AR, the AR 2 design with the tuning parameter c = (n /N)^0.1 was applied. Performances of 10 randomly selected trials are shown in gray lines for AR and in light blue lines for ER. The averages of 500,000 trials are also shown in the maroon line for AR and in the dark blue dashed line for ER. Response rates are p₁ = 0.2 and p₂ = 0.4. Panel (A): Without early stopping. The sample size of the AR 2 design sample size is 184; that of the ER design is 134, which is expanded to 184 after the trial is completed. Panel (B): With early stopping. The maximum sample size for the AR 2 design is 274 and for the ER design is 190. If a trial is stopped early (or completed for ER), additional patients are added to reach a total of 274 patients. Additional patients are allocated to the better treatment if the null hypothesis is rejected, or to the control arm if the null hypothesis is not rejected.

Figure 1

Randomization probability to the experimental treatment versus the number of patients accrued for two-arm trials with binary endpoints. The result of adaptive randomization (AR) and equal randomization (ER) are compared. For AR, the AR 2 design with the tuning parameter c = (n /N)^0.1 was applied. Performances of 10 randomly selected trials are shown in gray lines for AR and in light blue lines for ER. The averages of 500,000 trials are also shown in the maroon line for AR and in the dark blue dashed line for ER. Response rates are p₁ = 0.2 and p₂ = 0.4. Panel (A): Without early stopping. The sample size of the AR 2 design sample size is 184; that of the ER design is 134, which is expanded to 184 after the trial is completed. Panel (B): With early stopping. The maximum sample size for the AR 2 design is 274 and for the ER design is 190. If a trial is stopped early (or completed for ER), additional patients are added to reach a total of 274 patients. Additional patients are allocated to the better treatment if the null hypothesis is rejected, or to the control arm if the null hypothesis is not rejected.