We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples.

翻译：我们提出AFTNet，一种基于威布尔加速失效时间（AFT）模型的新型网络约束生存分析方法，该方法通过惩罚似然法进行变量选择与估计。采用对数线性表示时，推断问题转化为结构化稀疏回归问题，通过引入双重惩罚机制同时促进稀疏性与群组效应，显式整合预测变量间的相关模式。此外，我们建立了AFTNet估计量的理论一致性，并基于近端梯度下降法提出了一种高效的迭代计算算法。最后，我们通过合成数据与真实数据示例评估了AFTNet的性能。