Predicting time-to-event outcomes when event times are interval censored is challenging because the exact event time is unobserved. Many existing survival analysis approaches for interval-censored data rely on strong model assumptions or cannot handle high-dimensional predictors. We develop ICODEN, an ordinary differential equation-based neural network for interval-censored data that models the hazard function through deep neural networks and obtains the cumulative hazard by solving an ordinary differential equation. ICODEN does not require the proportional hazards assumption or a prespecified parametric form for the hazard function, thereby permitting flexible survival modeling. Across simulation settings with proportional or non-proportional hazards and both linear and nonlinear covariate effects, ICODEN consistently achieves satisfactory predictive accuracy and remains stable as the number of predictors increases. Applications to data from multiple phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to two Age-Related Eye Disease Studies (AREDS and AREDS2) for age-related macular degeneration (AMD) demonstrate ICODEN's robust prediction performance. In both applications, predicting time-to-AD or time-to-late AMD, ICODEN effectively uses hundreds to more than 1,000 SNPs and supports data-driven subgroup identification with differential progression risk profiles. These results establish ICODEN as a practical assumption-lean tool for prediction with interval-censored survival data in high-dimensional biomedical settings.

翻译：当事件时间呈区间删失时，预测事件发生时间具有挑战性，因为确切的事件时间未被观测到。现有许多针对区间删失数据的生存分析方法依赖于较强的模型假设，或无法处理高维预测因子。我们提出了ICODEN，一种基于常微分方程的区间删失数据神经网络，该网络通过深度神经网络对风险函数进行建模，并通过求解常微分方程获得累积风险。ICODEN无需比例风险假设，也不要求风险函数具有预设的参数形式，从而实现了灵活的生存建模。在包含比例风险与非比例风险、线性和非线性协变量效应的多种模拟设定中，ICODEN始终获得令人满意的预测精度，并在预测因子数量增加时保持稳定。在阿尔茨海默病神经影像倡议（ADNI）多阶段数据以及两项年龄相关性黄斑变性（AMD）的年龄相关性眼病研究（AREDS和AREDS2）数据中的应用表明，ICODEN具有稳健的预测性能。在预测AD发病时间或晚期AMD发病时间的两个应用中，ICODEN能有效利用数百至超过1000个SNP，并支持基于数据的亚组识别，以区分具有不同进展风险的特征。这些结果确立了ICODEN作为一种实用且假设宽松的工具，适用于高维生物医学环境中区间删失生存数据的预测。