The use of the non-parametric Restricted Mean Survival Time endpoint (RMST) has grown in popularity as trialists look to analyse time-to-event outcomes without the restrictions of the proportional hazards assumption. In this paper, we evaluate the power and type I error rate of the parametric and non-parametric RMST estimators when treatment effect is explained by multiple covariates, including an interaction term. Utilising the RMST estimator in this way allows the combined treatment effect to be summarised as a one-dimensional estimator, which is evaluated using a one-sided hypothesis Z-test. The estimators are either fully specified or misspecified, both in terms of unaccounted covariates or misspecified knot points (where trials exhibit crossing survival curves). A placebo-controlled trial of Gamma interferon is used as a motivating example to simulate associated survival times. When correctly specified, the parametric RMST estimator has the greatest power, regardless of the time of analysis. The misspecified RMST estimator generally performs similarly when covariates mirror those of the fitted case study dataset. However, as the magnitude of the unaccounted covariate increases, the associated power of the estimator decreases. In all cases, the non-parametric RMST estimator has the lowest power, and power remains very reliant on the time of analysis (with a later analysis time correlated with greater power).

翻译：非参数受限平均生存时间（RMST）终点的使用日益普及，因为试验研究者希望在无需比例风险假设限制的情况下分析时间-事件结局。本文在治疗效果由多个协变量（包括交互项）解释的情况下，评估了参数和非参数RMST估计量的统计功效和I类错误率。以这种方式使用RMST估计量，可将联合治疗效果汇总为一维估计量，并通过单侧假设Z检验进行评估。这些估计量在未纳入协变量或错误指定节点（当试验出现生存曲线交叉时）方面，既可能被完全正确指定，也可能被误设。以γ干扰素的安慰剂对照试验为例，模拟相关生存时间。当估计量被正确指定时，无论分析时间如何，参数RMST估计量均具有最大的统计功效。当协变量与拟合案例研究数据集中的协变量一致时，误设的RMST估计量通常表现相似。然而，随着未纳入协变量效应量的增大，估计量的统计功效相应下降。在所有情况下，非参数RMST估计量的统计功效最低，且功效高度依赖于分析时间（较晚的分析时间与更大的功效相关）。