Survival regression is widely used to model time-to-events data, to explore how covariates may influence the occurrence of events. Modern datasets often encompass a vast number of covariates across many subjects, with only a subset of the covariates significantly affecting survival. Additionally, subjects often belong to an unknown number of latent groups, where covariate effects on survival differ significantly across groups. The proposed methodology addresses both challenges by simultaneously identifying the latent sub-groups in the heterogeneous population and evaluating covariate significance within each sub-group. This approach is shown to enhance the predictive accuracy for time-to-event outcomes, via uncovering varying risk profiles within the underlying heterogeneous population and is thereby helpful to device targeted disease management strategies.

翻译：生存回归被广泛用于建模时间-事件数据，以探究协变量如何影响事件的发生。现代数据集通常包含大量受试者的众多协变量，其中仅有部分协变量对生存有显著影响。此外，受试者常属于未知数量的潜在群体，且协变量对生存的影响在不同群体间存在显著差异。本文提出的方法通过同时识别异质人群中的潜在亚群并评估各亚群内协变量的显著性，以应对上述双重挑战。该方法通过揭示底层异质人群中不同的风险特征，被证明能够提升时间-事件结局的预测准确性，从而有助于制定针对性的疾病管理策略。