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arXiv · 2303.01186

Discrete-time Competing-Risks Regression with or without Penalization

Tomer Meir·Malka Gorfine
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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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Tomer Meir, Malka Gorfine. 2023-03-02. Discrete-time Competing-Risks Regression with or without Penalization. https://doi.org/10.1093/biomtc%2Fujaf040

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