  This study aims to evaluate the performance of Cox regression with lasso
penalty and adaptive lasso penalty in high-dimensional settings. Variable
selection methods are necessary in this context to reduce dimensionality and
make the problem feasible. Several weight calculation procedures for adaptive
lasso are proposed to determine if they offer an improvement over lasso, as
adaptive lasso addresses its inherent bias. These proposed weights are based on
principal component analysis, ridge regression, univariate Cox regressions and
random survival forest (RSF). The proposals are evaluated in simulated
datasets.
  A real application of these methodologies in the context of genomic data is
also carried out. The study consists of determining the variables, clinical and
genetic, that influence the survival of patients with triple-negative breast
cancer (TNBC), which is a type breast cancer with low survival rates due to its
aggressive nature.
