We revisit the question of what randomization ratio (RR) maximizes power of the logrank test in event-driven survival trials under proportional hazards (PH). By comparing three approximations of the logrank test (Schoenfeld, Freedman, Rubinstein) to empirical simulations, we find that the RR that maximizes power is the RR that balances number of events across treatment arms at the end of the trial. This contradicts the common misconception implied by Schoenfeld's approximation that 1:1 randomization maximizes power. Besides power, we consider other factors that might influence the choice of RR (accrual, trial duration, sample size, etc.). We perform simulations to better understand how unequal randomization might impact these factors in practice. Altogether, we derive 6 insights to guide statisticians in the design of survival trials considering unequal randomization.

翻译：我们重新探讨了在比例风险假设下，事件驱动型生存试验中何种随机化比率能最大化时序检验的功效。通过比较时序检验的三种近似方法（Schoenfeld、Freedman、Rubinstein）与实证模拟结果，我们发现最大化功效的随机化比率是使试验结束时各治疗组间事件数达到均衡的比率。这反驳了由Schoenfeld近似法所暗示的常见误解，即1:1随机化能最大化功效。除功效外，我们还考虑了可能影响随机化比率选择的其他因素（患者入组速度、试验持续时间、样本量等）。我们通过模拟实验来更深入地理解非均衡随机化在实践中如何影响这些因素。综合而言，我们提出了六项启示，以指导统计学家在设计考虑非均衡随机化的生存试验时进行决策。