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 accelerates the estimation process; 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.

翻译：许多研究采用包含竞争风险和右删失的事件时间数据分析。多数方法和软件包适用于连续失效时间分布的数据。然而，由于时间本身具有离散性或测量不精确，失效时间数据有时可能呈现离散特征。本文提出了一种针对竞争事件离散时间生存分析的新型估计方法。该方法相较于现有程序具有两大优势：第一，可加速估计过程；第二，能够便捷地整合并应用广泛使用的正则化回归与筛选方法。我们通过综合模拟研究验证了所提方法的优势。此外，通过估计重症监护室住院患者住院时长的生存模型（考虑三种竞争事件：出院回家、转至其他医疗机构及院内死亡），展示了该程序的实际应用价值。