Department of Biostatistics, The University of Iowa, Iowa City, Iowa.
Department of Biostatistics, NYU School of Global Public Health, New York City, New York.
Stat Med. 2021 Apr 15;40(8):2024-2036. doi: 10.1002/sim.8888. Epub 2021 Feb 2.
Extensions of the Kaplan-Meier estimator have been developed to illustrate the relationship between a time-varying covariate of interest and survival. In particular, Snapinn et al and Xu et al developed estimators to display survival for patients who always have a certain value of a time-varying covariate. These estimators properly handle time-varying covariates, but their clinical interpretation is limited. It is of greater clinical interest to display survival for patients whose covariates lie along certain defined paths. In this article, we propose extensions of Snapinn et al and Xu et al's estimators, providing crude and covariate-adjusted estimates of the survival function for patients defined by covariate paths. We also derive analytical variance estimators. We demonstrate the utility of these estimators with medical examples and a simulation study.
Kaplan-Meier 估计的扩展已被开发出来,以说明时变协变量与生存之间的关系。特别是,Snapinn 等人和 Xu 等人开发了估计器,以显示始终具有特定时变协变量值的患者的生存情况。这些估计器正确地处理了时变协变量,但它们的临床解释是有限的。更具临床意义的是,显示沿着某些定义路径的协变量的患者的生存情况。在本文中,我们对 Snapinn 等人和 Xu 等人的估计器进行了扩展,为协变量路径定义的患者提供了生存函数的未调整和协变量调整估计值。我们还推导出了分析方差估计值。我们通过医学实例和模拟研究证明了这些估计器的实用性。