论文标题
与时间依赖的协变量有限的平均生存时间回归模型
Restricted mean survival time regression model with time-dependent covariates
论文作者
论文摘要
在临床或流行病学后续研究中，已经开发了基于时间尺度指标的方法，例如限制的平均生存时间（RMST）。与传统的危险率指标方法相比，RMST更容易解释，并且不需要比例危险假设。迄今为止，基于RMST的回归模型是RMST和基线协变量的间接模型或直接模型。但是，时间依赖性的协变量在后续研究中变得越来越普遍。基于审查加权方法的反概率（IPCW）方法，我们开发了RMST和时间依赖性协变量的回归模型。通过蒙特卡洛模拟，我们验证了所提出模型的回归参数的估计性能。与时间有关的COX模型和固定（基线）协变量RMST模型相比，时间依赖性RMST模型具有更好的预测能力。最后，使用心脏移植的例子来验证上述结论。
In clinical or epidemiological follow-up studies, methods based on time scale indicators such as the restricted mean survival time (RMST) have been developed to some extent. Compared with traditional hazard rate indicator system methods, the RMST is easier to interpret and does not require the proportional hazard assumption. To date, regression models based on the RMST are indirect or direct models of the RMST and baseline covariates. However, time-dependent covariates are becoming increasingly common in follow-up studies. Based on the inverse probability of censoring weighting (IPCW) method, we developed a regression model of the RMST and time-dependent covariates. Through Monte Carlo simulation, we verified the estimation performance of the regression parameters of the proposed model. Compared with the time-dependent Cox model and the fixed (baseline) covariate RMST model, the time-dependent RMST model has a better prediction ability. Finally, an example of heart transplantation was used to verify the above conclusions.
