In clinical follow-up studies with a time-to-event end point, the difference in the restricted mean survival time (RMST) is a suitable substitute for the hazard ratio (HR). However, the RMST only measures the survival of patients over a period of time from the baseline and cannot reflect changes in life expectancy over time. Based on the RMST, we study the conditional restricted mean survival time (cRMST) by estimating life expectancy in the future according to the time that patients have survived, reflecting the dynamic survival status of patients during follow-up. In this paper, we introduce the estimation method of cRMST based on pseudo-observations, the construction of test statistics according to the difference in the cRMST (cRMSTd), and the establishment of the robust dynamic prediction model using the landmark method. Simulation studies are employed to evaluate the statistical properties of these methods, which are also applied to two real examples. The simulation results show that the estimation of the cRMST is accurate and the cRMSTd test performs well. In addition, the dynamic RMST model has high accuracy in coefficient estimation and better predictive performance than the static RMST model. The hypothesis test proposed in this paper has a wide range of applicability, and the dynamic RMST model can predict patients' life expectancy from any prediction time, considering the time-dependent covariates and time-varying effects of covariates.

翻译：在具有时间至事件终点的临床随访研究中，受限平均生存时间（RMST）的差异是风险比（HR）的合适替代指标。然而，RMST仅能测量患者从基线开始一段时间内的生存情况，无法反映随时间变化的预期寿命变化。基于RMST，我们通过根据患者已存活时间估计未来预期寿命来研究条件受限平均生存时间（cRMST），从而反映患者随访期间的动态生存状态。本文介绍了基于伪观察值的cRMST估计方法、根据cRMST差异（cRMSTd）构建检验统计量，以及利用标杆法建立稳健动态预测模型的方法。采用模拟研究评估这些方法的统计特性，并将其应用于两个实际案例。模拟结果表明cRMST估计准确，cRMSTd检验表现良好。此外，动态RMST模型在系数估计方面具有较高准确性，且预测性能优于静态RMST模型。本文提出的假设检验具有广泛适用性，动态RMST模型能够考虑时依协变量及其时变效应，从任意预测时间点预测患者的预期寿命。