Discrete-time Competing-Risks Regression with or without Penalization

• URL: http://arxiv.org/abs/2303.01186v3
• Date: Wed, 05 Feb 2025 19:57:11 GMT
• Title: Discrete-time Competing-Risks Regression with or without Penalization
• Authors: Tomer Meir, Malka Gorfine,
• Abstract summary: We propose a new estimation procedure for discrete-time survival analysis with competing events. A Python package, PyDTS, is available for applying the proposed method with additional features.
• Score: 0.18416014644193068
• License:
• 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.

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