论文标题
连续时间数据的G计算和双重鲁棒标准化：与逆概率加权的比较
G-computation and doubly robust standardisation for continuous-time data: a comparison with inverse probability weighting
论文作者
论文摘要
在事件的时间设置中，G-Compuntion和双重稳健估计器基于离散的时间数据。但是，随着时间的流逝，许多生物过程正在不断发展。在本文中，我们将G计算和双重鲁棒标准化程序扩展到连续的时间上下文。我们将它们的性能与众所周知的逆概率加权（IPW）估计量进行比较，以使用模拟研究进行危害比和限制平均生存时间差的估计。在正确的模型规范下，所有方法都是公正的，但是G委托和双重鲁棒标准化比逆概率加权更有效。我们还分析了两个现实世界数据集，以说明这些方法的实际实现。我们已经更新了R软件包Risca，以促进这些方法的使用及其传播。
In time-to-event settings, g-computation and doubly robust estimators are based on discrete-time data. However, many biological processes are evolving continuously over time. In this paper, we extend the g-computation and the doubly robust standardisation procedures to a continuous-time context. We compare their performance to the well-known inverse-probability-weighting (IPW) estimator for the estimation of the hazard ratio and restricted mean survival times difference, using a simulation study. Under a correct model specification, all methods are unbiased, but g-computation and the doubly robust standardisation are more efficient than inverse probability weighting. We also analyse two real-world datasets to illustrate the practical implementation of these approaches. We have updated the R package RISCA to facilitate the use of these methods and their dissemination.
