  The penalized Cox proportional hazard model is a popular analytical approach
for survival data with a large number of covariates. Such problems are
especially challenging when covariates vary over follow-up time (i.e., the
covariates are time-dependent). The standard R packages for fully penalized Cox
models cannot currently incorporate time-dependent covariates. To address this
gap, we implement a variant of gradient descent algorithm (proximal gradient
descent) for fitting penalized Cox models. We apply our implementation to real
and simulated data sets.
