  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.
