PyDTS: A Python Package for Discrete-Time Survival Analysis with Competing Risks and Optional Penalization

TL;DR

PyDTS is an open-source Python package designed for discrete-time survival analysis with competing risks, offering regularized estimation, model evaluation, variable screening, and simulation tools to improve research accuracy.

Contribution

The paper introduces PyDTS, a comprehensive Python package that addresses the gap in tools for discrete-time survival analysis with competing risks, including regularization and simulation features.

Findings

Provides accurate analysis of discrete-time survival data with competing risks.

Includes regularized estimation methods to handle high-dimensional data.

Offers tools for model evaluation and variable screening.

Abstract

Time-to-event (survival) analysis models the time until a pre-specified event occurs. When time is measured in discrete units or rounded into intervals, standard continuous-time models can yield biased estimators. In addition, the event of interest may belong to one of several mutually exclusive types, referred to as competing risks, where the occurrence of one event prevents the occurrence or observation of the others. PyDTS is an open-source Python package for analyzing discrete-time survival data with competing-risks. It provides regularized estimation methods, model evaluation metrics, variable screening tools, and a simulation module to support research and development.