Aims Optimal sedation regimens for patients after extracorporeal cardiopulmonary
resuscitation (ECPR) remain unclear. This study compared the outcomes of patients who …

With the massive incidence of cancer in recent centuries, it is crucial to carefully analyze the
recorded information and provide a thought-out plan for patients' treatment. A prevalent type …

PURPOSE Treatment decisions about localized prostate cancer depend on accurate
estimation of the patient's life expectancy. Current cancer and noncancer survival models …

Variable selection is critical in competing risks regression with high‐dimensional data.
Although penalized variable selection methods and other machine learning‐based …

In analysis of survival outcomes supplemented with both clinical information and high-
dimensional gene expression data, use of the traditional Cox proportional hazards model …

This article develops two orthogonal contributions to scalable sparse regression for
competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge …

Variable selection and penalized regression models in high‐dimension settings have
become an increasingly important topic in many disciplines. For instance, omics data are …

The purpose of this paper is to construct confidence intervals for the regression coefficients
in the Fine-Gray model for competing risks data with random censoring, where the number …

Competing risks data usually arise when an occurrence of an event precludes other types of
events from being observed. Such data are often encountered in a clustered clinical study …

In chapter 1 we study explained variation under the additive hazards regression model for
right-censored data. We consider different approaches for developing such a measure, and …