Background Risk prediction models for time-to-event outcomes play a vital role in
personalized decision-making. A patient's biomarker values, such as medical lab results, are …

In nephrology, a great deal of information is measured repeatedly in patients over time, often
alongside data on events of clinical interest. In this introductory article we discuss how these …

In medical studies, time-to-event outcomes such as time to death or relapse of a disease are
routinely recorded along with longitudinal data that are observed intermittently during the …

Clustering longitudinal features is a common goal in medical studies to identify distinct
disease developmental trajectories. Compared to clustering a single longitudinal feature …

Joint models are an increasingly popular way to characterise the relationship between one
or more longitudinal responses and an event of interest. However, for multivariate joint …

Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes,
which are correlated and predictive of AD progression. It is of great scientific interest to …

Joint models for longitudinal and survival data have become an established tool for
optimally handling scenarios when both types of data co-exist. Multivariate extensions to the …

Abstract Quasi-Monte Carlo (QMC) methods using quasi-random sequences, as opposed to
pseudo-random samples, are proposed for use in the joint modelling of time-to-event and …

Simple Summary Colorectal cancer is the fourth most common cancer and second most
common cause of cancer-death in the UK. If diagnosed and treated early-stage, when the …

The aim of dynamic prediction is to provide individualized risk predictions over time, which
are updated as new data become available. In pursuit of constructing a dynamic prediction …