Currently available risk prediction methods are limited in their ability to deal with complex,
heterogeneous, and longitudinal data such as that available in primary care records, or in …

In medicine, survival analysis studies the time duration to events of interest such as mortality.
One major challenge is how to deal with multiple competing events (eg, multiple disease …

When modelling competing risks survival data, several techniques have been proposed in
both the statistical and machine learning literature. State-of-the-art methods have extended …

The inclusion of time-varying covariates into survival analysis has led to better predictions of
the time to default in behavioural credit scoring models. However, when these time-varying …

Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-
pharmaceutical interventions (NPIs) were implemented by the cantons and the federal …

Background The currently available prediction models, such as the Cox model, were too
simplistic to correctly predict the outcome of gastric adenocarcinoma patients. This study …

When dealing with right-censored data, where some outcomes are missing due to a limited
observation period, survival analysis--known as time-to-event analysis--focuses on …

Abstract Competing Risk Analysis (CRA) aims at the correct estimation of the marginal
probability of occurrence of an event in the presence of competing events. Many of the …

When data are right-censored, ie some outcomes are missing due to a limited period of
observation, survival analysis can compute the" time to event". Multiple classes of outcomes …

We study the problem of inferring heterogeneous treatment effects (HTEs) from time-to-event
data in the presence of competing events. Albeit its great practical relevance, this problem …