Discrete-time Competing-Risks Regression with or without Penalization

TL;DR

This paper introduces a novel estimation method for discrete-time competing-risks survival analysis, enabling integration with regularized regression techniques and demonstrated through simulations and ICU patient data.

Contribution

It presents a new estimation procedure tailored for discrete-time competing risks data, compatible with regularization methods, filling a gap in existing survival analysis tools.

Findings

The method performs well in simulation studies.

It effectively models ICU patient length of stay with competing risks.

The Python package PyDTS facilitates practical application.

Abstract

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 new estimation procedure for discrete-time survival analysis with competing events. The proposed approach offers a major key advantage over existing procedures and allows for straightforward integration and application of widely used regularized regression and screening-features methods. We illustrate the benefits of our proposed approach by a comprehensive simulation study. Additionally, we showcase the utility of the proposed 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. A Python package, PyDTS, is available for applying the proposed method with additional features.