Estimating survival functions after stcox with time-varying coefficients
Estimating survival functions after stcox with time-varying coefficients
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DOI：
10.1177/1536867x1601600404
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发表时间：
2016-01-01
期刊：
影响因子：
4.8
通讯作者：
Ruhe, Constantin
中科院分区：
文献类型：
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作者：
Ruhe, Constantin
在考克斯模型的许多应用中，比例风险假设是不可信的。在这些情况下，非比例风险的解决方案通常包括对感兴趣变量的影响及其与时间的交互作用进行建模。虽然Stata提供了一个命令来在stcox中实现这种交互，但是如果stcox是用tvc（）选项估计的，它不允许使用stcurve进行典型的可视化。在这篇文章中，我提供了一个简短的解决方案，估计stcox后的生存函数与时间相关的系数。我介绍并描述了scurve_tvc命令，它可以自动执行此过程，并允许用户轻松地可视化具有时变效应的模型的生存函数。
In many applications of the Cox model, the proportional-hazards assumption is implausible. In these cases, the solution to nonproportional hazards usually consists of modeling the effect of the variable of interest and its interaction effect with some function of time. Although Stata provides a command to implement this interaction in stcox, it does not allow the typical visualizations using stcurve if stcox was estimated with the tvc() option. In this article, I provide a short workaround that estimates the survival function after stcox with time dependent coefficients. I introduce and describe the scurve_tvc command, which automates this procedure and allows users to easily visualize survival functions for models with time-varying effects.