  Time-to-event analysis (survival analysis) is used when the response of
interest is the time until a pre-specified event occurs. Time-to-event data are
sometimes discrete either because time itself is discrete or due to grouping of
failure times into intervals or rounding off measurements. In addition, the
failure of an individual could be one of several distinct failure types, known
as competing risks (events). Most methods and software packages for survival
regression analysis assume that time is measured on a continuous scale. It is
well-known that naively applying standard continuous-time models with
discrete-time data may result in biased estimators of the discrete-time models.
The Python package PyDTS, for simulating, estimating and evaluating
semi-parametric competing-risks models for discrete-time survival data, is
introduced. The package implements a fast procedure that enables including
regularized regression methods, such as LASSO and elastic net, among others. A
simulation study showcases flexibility and accuracy of the package. The utility
of the package is demonstrated by analysing the Medical Information Mart for
Intensive Care (MIMIC) - IV dataset for prediction of hospitalization length of
stay.
