Time-varying covariates are often available in survival studies, and estimation of the hazard function needs to be updated as new information becomes available. In this article, we investigate several different easy-to-implement ways that random forests can be used for dynamic estimation of the survival or hazard function from discrete-time survival data. Results from a simulation study indicate that all methods can perform well, and that none dominates the others. In general, situations that are more difficult from an estimation point of view (such as weaker signals and less data) favour a global fit, pooling over all time points, while situations that are easier from an estimation point of view (such as stronger signals and more data) favour local fits.

中文翻译：

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离散时间生存数据的随机森林动态估计

随时间变化的协变量通常在生存研究中可用，并且随着新信息的可用，需要更新对风险函数的估计。在本文中，我们研究了几种不同的易于实现的方法，这些方法可以使用随机森林从离散时间生存数据中动态估计生存或风险函数。模拟研究的结果表明，所有方法都可以很好地执行，并且没有一种方法可以支配其他方法。一般来说，从估计的角度来看更困难的情况（例如较弱的信号和较少的数据）有利于全局拟合，汇集所有时间点，而从估计的角度来看更容易的情况（例如更强的信号和更多数据）有利于局部拟合。