Discrete-time Competing-Risks Regression with or without Penalization

• URL: http://arxiv.org/abs/2303.01186v2
• Date: Tue, 14 Nov 2023 19:45:52 GMT
• Title: Discrete-time Competing-Risks Regression with or without Penalization
• Authors: Tomer Meir and Malka Gorfine
• Abstract summary: This paper introduces a novel estimation procedure for discrete-time survival analysis with competing events. We illustrate the benefits of our proposed approach by conducting a comprehensive simulation study.
• Score: 0.21756081703276003
• License: http://creativecommons.org/licenses/by/4.0/
• 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 novel estimation procedure for discrete-time survival analysis with competing events. The proposed approach offers two key advantages over existing procedures: first, it expedites the estimation process for a large number of unique failure time points; second, it allows for straightforward integration and application of widely used regularized regression and screening methods. We illustrate the benefits of our proposed approach by conducting a comprehensive simulation study. Additionally, we showcase the utility of our 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.

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