  We propose an iterative variable selection method for the accelerated failure
time model using high-dimensional survival data. Our method pioneers the use of
the recently proposed structured screen-and-select framework for survival
analysis. We use the marginal utility as the measure of association to inform
the structured screening process. For the selection steps, we use Bayesian
model selection based on non-local priors. We compare the proposed method with
a few well-known methods. Assessment in terms of true positive rate and false
discovery rate shows the usefulness of our method. We have implemented the
method within the R package GWASinlps.
