In observational studies, the time origin of interest for time-to-event analysis is often
unknown, such as the time of disease onset. Existing approaches to estimating the time …

Often, in follow‐up studies, patients experience intermediate events, such as reinterventions
or adverse events, which directly affect the shapes of their longitudinal profiles. Our work is …

We propose a class of landmark cure rate models by incorporating time-dependent
covariates. The landmark approach enables us to obtain dynamic predictions of a patient's …

Background The individual data collected throughout patient follow-up constitute crucial
information for assessing the risk of a clinical event, and eventually for adapting a …

In longitudinal studies where time to a final event is the ultimate outcome often information is
available about intermediate events the individuals may experience during the observation …

With the availability of massive amounts of data from electronic health records and registry
databases, incorporating time-varying patient information to improve risk prediction has …

Simulations and Monte Carlo methods serve an important role in modern statistical
research. They allow for an examination of the performance of statistical procedures in …

Predicting the occurrence of an event over time for a newly diagnosed individual is a
common aim in medical statistics. For time-to-event outcomes, this prediction is typically …

We consider the joint modelling of longitudinal and event time data. The longitudinal data
are irregularly collected and the event times are subject to right censoring. Most methods …

Background The recent progress in medical research generates an increasing interest in the
use of longitudinal biomarkers for characterizing the occurrence of an outcome. The present …