Machine learning can be used to make sense of healthcare data. Probabilistic machine
learning models help provide a complete picture of observed data in healthcare. In this …

We propose a reinforcement learning method for estimating an optimal dynamic treatment
regime for survival outcomes with dependent censoring. The estimator allows the failure …

Aims To investigate an innovative pharmacometrics approach that addresses the challenges
of using real‐world evidence to model the progression of illicit substance use. Methods The …

Survival analysis is a fundamental area of focus in biomedical research, particularly in the
context of personalized medicine. This prominence is due to the increasing prevalence of …

Background: Mild cognitive impairment (MCI) patients are at a high risk of developing
Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10 …

Censored data, where the event time is partially observed, are challenging for survival
probability estimation. In this article, we introduce a novel nonparametric fiducial approach …

Censored quantile regression has emerged as a prominent alternative to classical Cox's
proportional hazards model or accelerated failure time model in both theoretical and applied …

In recent years, censored quantile regression has enjoyed an increasing popularity for
survival analysis while many existing works rely on linearity assumptions. In this work, we …

Data following an interval structure are increasingly prevalent in many scientific applications.
In medicine, clinical events are often monitored between two clinical visits, making the exact …

In survival analysis, estimating the failure time distribution is an important and difficult task,
since usually the data is subject to censoring. Specifically, in this paper we consider current …