Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints

An equal randomization ratio (1:1) is the most commonly used allocation strategy in confirmatory clinical trials. Recently, there has been growing discussion around the use of unequal randomization, which may be favored for several reasons, including encouraging trial recruitment, reducing costs, and improving the robustness of estimates in the treatment arm. However, despite these potential benefits, unequal randomization is still rarely applied in trial design. A key barrier is the lack of a method to determine the optimal randomization ratio that balances the various considerations of a trial. To address this challenge, we developed an optimization framework that identifies the optimal randomization ratio to maximize a trial's probability of success and profit-two major considerations in trial design. The framework incorporates trial parameters such as prior knowledge of treatment efficacy, sample size, budget, and per-subject cost. We evaluate the proposed method through simulations and a hypothetical trial. The results show that the optimal randomization ratio is highly influenced by sample size and cost differences between treatment arms. Our framework demonstrates the ability to reduce costs while maintaining a high probability of success.