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Many studies employ the analysis of time-to-event data that incorporatescompeting risks and right censoring. Most methods and software packages aregeared towards analyzing data that comes from a continuous failure timedistribution. However, failure-time data may sometimes be discrete eitherbecause time is inherently discrete or due to imprecise measurement. This paperintroduces a novel estimation procedure for discrete-time survival analysiswith competing events. The proposed approach offers two key advantages overexisting procedures: first, it expedites the estimation process for a largenumber of unique failure time points; second, it allows for straightforwardintegration and application of widely used regularized regression and screeningmethods. We illustrate the benefits of our proposed approach by conducting acomprehensive simulation study. Additionally, we showcase the utility of ourprocedure by estimating a survival model for the length of stay of patientshospitalized in the intensive care unit, considering three competing events:discharge to home, transfer to another medical facility, and in-hospital death.

Discrete-time Competing-Risks Regression with or without Penalization