Restricted mean survival time (RMST) is a clinically interpretable and meaningful survival metric that has gained popularity in recent years. Several methods are available for regression modeling of RMST, most based on pseudo-observations or what is essentially an inverse-weighted complete-case analysis. No existing RMST regression method allows for the covariate effects to be expressed as functions over time. This is a considerable limitation, in light of the many hazard regression methods that do accommodate such effects. To address this void in the literature, we propose RMST methods that permit estimating time-varying effects. In particular, we propose an inference framework for directly modeling RMST as a continuous function of L. Large-sample properties are derived. Simulation studies are performed to evaluate the performance of the methods in finite sample sizes. The proposed framework is applied to kidney transplant data obtained from the Scientific Registry of Transplant Recipients.

中文翻译：

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作为限制时间的函数的限制平均生存时间

受限平均生存时间 (RMST) 是一种临床可解释且有意义的生存指标，近年来已广受欢迎。有几种方法可用于 RMST 的回归建模，大多数基于伪观察或本质上是逆加权完整案例分析。没有现有的 RMST 回归方法允许将协变量效应表示为随时间变化的函数。这是一个相当大的限制，因为许多危害回归方法确实适应了这种影响。为了解决文献中的这一空白，我们提出了允许估计时变效应的 RMST 方法。特别是，我们提出了一个推理框架，用于直接将 RMST 建模为L的连续函数. 导出大样本属性。进行模拟研究以评估方法在有限样本量中的性能。提议的框架适用于从移植受者科学登记处获得的肾移植数据。