Background/Objectives: Results from a well-designed trial provide evidence to support approval of truly effective treatments or discontinuation of ineffective treatments. However, the information available at the time of trial design may be limited which may lead to underpowered trials. This work aims to evaluate the impact of design assumption misspecifications on the statistical power of randomized trials with survival outcomes.Methods: The impact of the design assumption misspecifications on statistical power of four different statistical methods was investigated in a simulation study. The methods include the log-rank test, MaxCombo test, the test of difference in survival probability, and test of difference in restricted mean survival time (RMST). The deviations considered include the survival rate in the control arm, the expected treatment effect in terms of magnitude and pattern, accrual rate, and drop-out rate.Results: Deviations in the control arm’s survival distribution have no impact on the power of the log-rank and MaxCombo tests but it affects the trial duration since trials designed with these tests require the total number of events to be met before the final analysis can be conducted. Misspecified treatment effect has similar effect on the statistical power of all four methods. When the proportional hazards assumption is misspecified, the RMST is more robust with a larger early treatment effect, while the survival probability and the MaxCombo tests are more robust with a larger late treatment effect and crossing hazards.Conclusions: Selecting the appropriate statistical tests to design a trial depends on the goal of the trial, the mechanism of action of the experimental treatment, the survival quantity of clinical interest, and the pattern of the expected treatment effect. The final design should be based on assumptions that are as accurate as possible, and the potential impacts of deviations from these assumptions on the trial’s statistical power should be carefully considered.
背景/目的：设计良好的试验结果可为批准真正有效的治疗方案或终止无效治疗提供证据支持。然而，试验设计阶段可获取的信息可能有限，这可能导致试验效能不足。本研究旨在评估设计假设误设对生存结局随机试验统计效能的影响。 方法：通过模拟研究，探讨了设计假设误设对四种不同统计方法统计效能的影响。这些方法包括对数秩检验、MaxCombo检验、生存概率差异检验以及限制性平均生存时间（RMST）差异检验。考虑的偏差因素包括对照组生存率、预期治疗效果（包括效应大小和模式）、入组率和失访率。 结果：对照组生存分布的偏差不影响对数秩检验和MaxCombo检验的效能，但会影响试验持续时间，因为采用这些检验方法设计的试验需在最终分析前达到预设的事件总数。治疗效果误设对所有四种方法的统计效能影响相似。当比例风险假设误设时，RMST检验在早期治疗效果较大时更具稳健性，而生存概率检验和MaxCombo检验在晚期治疗效果较大及风险函数交叉时表现更稳健。 结论：选择适当的统计检验方法设计试验，需综合考虑试验目标、实验性治疗的作用机制、临床关注的生存指标以及预期治疗效应的模式。最终设计方案应基于尽可能准确的假设，并需审慎评估假设偏差对试验统计效能的潜在影响。
The Impact of Design Misspecifications on Survival Outcomes in Cancer Clinical Trials
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