Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine. This prominence is due to the increasing prevalence of large and high-dimensional datasets, such as omics and medical image data. However, the literature on non-linear regression algorithms and variable selection techniques for interval-censoring is either limited or non-existent, particularly in the context of neural networks. Our objective is to introduce a novel predictive framework tailored for interval-censored regression tasks, rooted in Accelerated Failure Time (AFT) models. Our strategy comprises two key components: i) a variable selection phase leveraging recent advances on sparse neural network architectures, ii) a regression model targeting prediction of the interval-censored response. To assess the performance of our novel algorithm, we conducted a comprehensive evaluation through both numerical experiments and real-world applications that encompass scenarios related to diabetes and physical activity. Our results outperform traditional AFT algorithms, particularly in scenarios featuring non-linear relationships.

翻译：生存分析是生物医学研究中的一个基础重点领域，尤其在个性化医疗的背景下。其重要性源于大规模高维数据集（如组学数据和医学影像数据）日益普遍。然而，针对区间删失的非线性回归算法和变量选择技术的文献十分有限甚至缺失，特别是在神经网络领域。我们的目标是提出一种专为区间删失回归任务设计的新型预测框架，该框架基于加速失效时间模型。我们的策略包含两个关键组成部分：i) 利用稀疏神经网络架构最新进展的变量选择阶段，ii) 以预测区间删失响应为目标的回归模型。为评估新算法的性能，我们通过数值实验和涵盖糖尿病与体力活动相关场景的实际应用进行了全面评估。我们的结果优于传统的加速失效时间算法，特别是在存在非线性关系的场景中。