Background: For RCTs with time-to-event endpoints, proportional hazard (PH) models are typically used to estimate treatment effects and logrank tests are commonly used for hypothesis testing. There is growing support for replacing this approach with a model-free estimand and assumption-lean analysis method. One alternative is to base the analysis on the difference in restricted mean survival time (RMST) at a specific time, a single-number summary measure that can be defined without any restrictive assumptions on the outcome model. In a simple setting without covariates, an assumption-lean analysis can be achieved using nonparametric methods such as Kaplan Meier estimation. The main advantage of moving to a model-free summary measure and assumption-lean analysis is that the validity and interpretation of conclusions do not depend on the PH assumption. The potential disadvantage is that the nonparametric analysis may lose efficiency under PH. There is disagreement in recent literature on this issue. Methods: Asymptotic results and simulations are used to compare the efficiency of a log-rank test against a nonparametric analysis of the difference in RMST in a superiority trial under PH. Previous studies have separately examined the effect of event rates and the censoring distribution on relative efficiency. This investigation clarifies conflicting results from earlier research by exploring the joint effect of event rate and censoring distribution together. Several illustrative examples are provided. Results: In scenarios with high event rates and/or substantial censoring across a large proportion of the study window, and when both methods make use of the same amount of data, relative efficiency is close to unity. However, in cases with low event rates but when censoring is concentrated at the end of the study window, the PH analysis has a considerable efficiency advantage.

翻译：背景：对于以时间-事件为终点的随机对照试验，通常使用比例风险模型估计治疗效果，并常用对数秩检验进行假设检验。目前，越来越多的学者支持采用无模型估计量和假设宽松的分析方法来替代这一传统方法。一种替代方案是基于特定时间点限制平均生存时间差异进行分析，该单一数值汇总指标无需对结果模型施加任何限制性假设即可定义。在无协变量的简单设定下，可采用Kaplan-Meier估计等非参数方法实现假设宽松的分析。转向无模型汇总指标和假设宽松分析的主要优势在于：结论的有效性和解释不依赖于比例风险假设。其潜在劣势在于，非参数分析在比例风险条件下可能损失效率。近期文献对此问题存在分歧。方法：本研究通过渐近结果与模拟实验，比较了在比例风险条件下优效性试验中，对数秩检验与基于RMST差异的非参数分析的效率。先前研究已分别考察了事件发生率和删失分布对相对效率的影响。本研究通过联合探究事件发生率与删失分布的共同作用，澄清了早期研究中的矛盾结论，并提供了若干示例说明。结果：在事件发生率较高和/或研究窗口大部分时段存在显著删失，且两种方法使用相同数据量的情境下，相对效率接近1。然而，在事件发生率较低但删失集中于研究窗口末期的情况下，比例风险分析具有显著的效率优势。