Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (i.e., covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. Sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.

翻译：具有时变系数和协变量的Cox模型在生存分析中被广泛应用。在高维场景下，稀疏正则化技术常用于变量选择，但现有针对时变Cox模型的方法在强制实现特定稀疏模式（即协变量结构）方面缺乏灵活性。我们提出了一种灵活的框架，用于时变Cox模型中的变量选择，可适应复杂的筛选规则。该方法能适配任意分组结构，包括交互作用选择、时间结构、空间结构、树结构以及有向无环图结构。它能够实现精确估计且误报率较低。我们开发了sox软件包，通过实现网络流算法高效求解具有复杂协变量结构的模型。Sox为指定分组结构提供了用户友好的接口，并可实现快速计算。通过多个示例，包括一项识别房颤患者全因死亡时间预测因子的案例研究，我们展示了所提方法在具体筛选规则下的实际应用。