5894New York University, New York, NY, USA.
HEC Montréal, Montréal, Québec, CA.
Stat Methods Med Res. 2022 Nov;31(11):2217-2236. doi: 10.1177/09622802221111549. Epub 2022 Jul 27.
Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of a survival function. However, the traditional survival forests-conditional inference forest, relative risk forest and random survival forest-have accommodated only time-invariant covariates. We generalize the conditional inference and relative risk forests to allow time-varying covariates. We also propose a general framework for estimation of a survival function in the presence of time-varying covariates. We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated difference between the true and estimated survival functions. In general, the performance of the two proposed forests substantially improves over the Kaplan-Meier estimate. Taking into account all other factors, under the proportional hazard setting, the best method is always one of the two proposed forests, while under the non-proportional hazard setting, it is the adapted transformation forest. -fold cross-validation is used as an effective tool to choose between the methods in practice.
在实践中，具有时变协变量的生存数据很常见。如果相关，它们可以提高生存函数的估计。然而，传统的生存森林——条件推断森林、相对风险森林和随机生存森林——仅允许时不变协变量。我们将条件推断和相对风险森林推广到允许时变协变量的情况。我们还提出了一个在存在时变协变量的情况下估计生存函数的通用框架。我们通过综合模拟研究将它们的性能与 Cox 模型和转换森林进行了比较，在这里，转换森林经过了修改以适应时变协变量，使用真实和估计生存函数之间的综合差异来比较性能。一般来说，这两种提议的森林的性能大大优于 Kaplan-Meier 估计。考虑到所有其他因素，在比例风险设置下，最好的方法始终是两种提议的森林之一，而在非比例风险设置下，最好的方法是修改后的转换森林。 -折交叉验证是在实践中选择方法的有效工具。
