Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China.
Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, Guangzhou, China.
Biometrics. 2023 Dec;79(4):3690-3700. doi: 10.1111/biom.13891. Epub 2023 Jun 19.
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 statistical inference concerning the difference between two cRMSTs (cRMSTd), and the establishment of the robust dynamic prediction model using the landmark method. Simulation studies are conducted to evaluate the statistical properties of these methods. The results indicate that the estimation of the cRMST is accurate, and the dynamic RMST model has high accuracy in coefficient estimation and good predictive performance. In addition, an example of patients with chronic kidney disease who received renal transplantations is employed to illustrate that the dynamic RMST model can predict patients' expected survival times from any prediction time, considering the time-dependent covariates and time-varying effects of covariates.
在具有事件时间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）的合适替代物。然而，RMST 仅测量从基线开始一段时间内的患者生存情况，无法反映随时间推移的预期寿命变化。基于 RMST，我们通过根据患者存活的时间来估计未来的预期寿命来研究条件受限平均生存时间（cRMST），反映了患者在随访期间的动态生存状况。在本文中，我们介绍了基于伪观测的 cRMST 估计方法、两个 cRMST 之间差异的统计推断（cRMSTd）以及使用 landmark 方法建立稳健的动态预测模型。进行了模拟研究以评估这些方法的统计特性。结果表明，cRMST 的估计是准确的，动态 RMST 模型在系数估计和良好的预测性能方面具有高精度。此外，还使用接受肾移植的慢性肾脏病患者的实例说明了动态 RMST 模型可以从任何预测时间预测患者的预期生存时间，同时考虑了随时间变化的协变量和协变量的时变效应。
