Survival Analysis (SA) constitutes the default method for time-to-event modeling due to its ability to estimate event probabilities of sparsely occurring events over time. In this work, we show how to improve the training and inference of SA models by decoupling their full expression into (1) an aggregated baseline hazard, which captures the overall behavior of a given population, and (2) independently distributed survival scores, which model idiosyncratic probabilistic dynamics of its given members, in a fully parametric setting. The proposed inference method is shown to dynamically handle right-censored observation horizons, and to achieve competitive performance when compared to other state-of-the-art methods in a variety of real-world datasets, including computationally inefficient Deep Learning-based SA methods and models that require MCMC for inference. Nevertheless, our method achieves robust results from the outset, while not being subjected to fine-tuning or hyperparameter optimization.

翻译：生存分析（Survival Analysis, SA）是处理时间至事件建模的默认方法，能够估计稀疏发生事件随时间变化的概率。本研究提出通过将完整表达式解耦为（1）捕获群体整体行为的聚合基线风险函数，与（2）独立分布、建模个体特异概率动力学的生存得分，在全参数化框架下改进生存分析模型的训练与推理过程。所提出的推理方法能动态处理右删失观测窗口，并在多个真实世界数据集中展现出与最新方法（包括计算效率低下的深度学习方法及需MCMC推理的模型）相当的竞争力，在无需微调或超参数优化的条件下即可获得稳定结果。