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 https://github.com/ssokota/spie .

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