  Many studies employ the analysis of time-to-event data that incorporates
competing risks and right censoring. Most methods and software packages are
geared towards analyzing data that comes from a continuous failure time
distribution. However, failure-time data may sometimes be discrete either
because time is inherently discrete or due to imprecise measurement. This paper
introduces a novel estimation procedure for discrete-time survival analysis
with competing events. The proposed approach offers two key advantages over
existing procedures: first, it expedites the estimation process for a large
number of unique failure time points; second, it allows for straightforward
integration and application of widely used regularized regression and screening
methods. We illustrate the benefits of our proposed approach by conducting a
comprehensive simulation study. Additionally, we showcase the utility of our
procedure by estimating a survival model for the length of stay of patients
hospitalized in the intensive care unit, considering three competing events:
discharge to home, transfer to another medical facility, and in-hospital death.

Methodology;Machine Learning