Background In clinical research, there is an increasing interest in joint modelling of
longitudinal and time-to-event data, since it reduces bias in parameter estimation and …

Time-varying covariance occurs when a covariate changes over time during the follow-up
period. Such variable can be analyzed with the Cox regression model to estimate its effect …

Objectives To systematically identify and qualitatively review the statistical approaches used
in prospective cohort studies of team sports that reported intensive longitudinal data (ILD)(> …

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 …

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 …

Accurate prediction of disease trajectories is critical for early identification and timely
treatment of patients at risk. Conventional methods in survival analysis are often constrained …

This study attempted to construct and validate dynamic prediction via multivariate joint
models and compare the prognostic performance of these models to both static and …

As an organism ages, its health-state is determined by a balance between the processes of
damage and repair. Measuring these processes requires longitudinal data. We extract …

Joint modelling of longitudinal and time-to-event data has received much attention recently.
Increasingly, extensions to standard joint modelling approaches are being proposed to …

Background Joint modelling of longitudinal and time-to-event outcomes has received
considerable attention over recent years. Commensurate with this has been a rise in …