论文标题
PYDTS：一个用于离散时间生存的Python套餐（正规化）回归和竞争风险
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing Risks
论文作者
论文摘要
当感兴趣的响应是发生预先指定事件的时间时，使用了事件时间分析（生存分析）。有时由于时间本身是离散的，或者是由于将故障时间分为间隔或舍入测量结果，因此有时是离散的。此外，一个人的失败可能是几种不同的失败类型之一，称为竞争风险（事件）。大多数用于生存回归分析的方法和软件包都假定时间是在连续尺度上测量的。众所周知，天真地应用具有离散时间数据的标准连续时模型可能会导致离散时间模型的偏差。介绍了用于模拟，估计和评估半参数竞争风险模型的Python软件包PYDT，以用于离散时间生存数据。该软件包实施了一个快速的程序，该程序可以包括正规化回归方法，例如套索和弹性网等。一项模拟研究展示了包装的灵活性和准确性。通过分析重症监护室（MIMIC）-IV数据集的医疗信息MART来预测住院时间的时间，可以证明包装的效用。
Time-to-event analysis (survival analysis) is used when the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time itself is discrete or due to grouping of failure times into intervals or rounding off measurements. In addition, the failure of an individual could be one of several distinct failure types, known as competing risks (events). Most methods and software packages for survival regression analysis assume that time is measured on a continuous scale. It is well-known that naively applying standard continuous-time models with discrete-time data may result in biased estimators of the discrete-time models. The Python package PyDTS, for simulating, estimating and evaluating semi-parametric competing-risks models for discrete-time survival data, is introduced. The package implements a fast procedure that enables including regularized regression methods, such as LASSO and elastic net, among others. A simulation study showcases flexibility and accuracy of the package. The utility of the package is demonstrated by analysing the Medical Information Mart for Intensive Care (MIMIC) - IV dataset for prediction of hospitalization length of stay.
