Accurate models of patient survival probabilities provide important
information to clinicians prescribing care for life-threatening and terminal
ailments. A recently developed class of models - known as individual survival
distributions (ISDs) - produces patient-specific survival functions that offer
greater descriptive power of patient outcomes than was previously possible.
Unfortunately, at the time of writing, ISD models almost universally lack
uncertainty quantification. In this paper, we demonstrate that an existing
method for estimating simultaneous prediction intervals from samples can easily
be adapted for patient-specific survival curve analysis and yields accurate
results. Furthermore, we introduce both a modification to the existing method
and a novel method for estimating simultaneous prediction intervals and show
that they offer competitive performance. It is worth emphasizing that these
methods are not limited to survival analysis and can be applied in any context
in which sampling the distribution of interest is tractable. Code is available
at this https URL .

本研究旨在开发一种患者个体特征存活函数的预测模型，提供更准确的生存概率估计，并演示可将现有的样本预测区间估计方法应用于此类生存曲线分析中，同时还介绍了改进预测区间估计方法和新的预测区间估计方法，表现出竞争性的性能，这些方法不仅能用于生存分析，还可以应用于任何可跟踪目标分布的情景中。

患者个体生存曲线的同时预测区间

Simultaneous Prediction Intervals for Patient-Specific Survival Curves

We establish the first nonasymptotic error bounds for Kaplan-Meier-based
nearest neighbor and kernel survival probability estimators where feature
vectors reside in metric spaces. Our bounds imply rates of strong consistency
for these nonparametric estimators and, up to a log factor, match an existing
lower bound for conditional CDF estimation. Our proof strategy also yields
nonasymptotic guarantees for nearest neighbor and kernel variants of the
Nelson-Aalen cumulative hazards estimator. We experimentally compare these
methods on four datasets. We find that for the kernel survival estimator, a
good choice of kernel is one learned using random survival forests.

通过建立第一个 Kaplan-Meier 基础的最近邻和核生存概率估计的不渐近误差界，我们证明了这些非参数估计的强一致性速率，并且匹配了现有条件 CDF 估计的下限；我们的证明策略还为 Nelson-Aalen 累积风险估计的最近邻和核变体提供了不渐近保证。我们在四个数据集上进行了实验比较这些方法，并发现对于核生存估计器，一个好的核的选择是使用随机生存森林学习的核。