In longitudinal cohort studies, participants are often monitored through periodic clinical visits
until the occurrence of a terminal clinical event. A question of interest to both scientific …

Joint modeling and landmark modeling are two mainstream approaches to dynamic
prediction in longitudinal studies, that is, the prediction of a clinical event using longitudinally …

We propose an extension of the landmark model for ordinary survival data as a new
approach to the problem of dynamic prediction in competing risks with time‐dependent …

Dynamic prediction incorporates time‐dependent marker information accrued during follow‐
up to improve personalized survival prediction probabilities. At any follow‐up, or “landmark” …

In clinical research and practice, landmark models are commonly used to predict the risk of
an adverse future event, using patients' longitudinal biomarker data as predictors. However …

In this article, we propose a new approach to the problem of dynamic prediction of survival
data in the presence of competing risks as an extension of the landmark model for ordinary …

This article advocates the landmarking approach that dynamically adjusts predictive models
for survival data during the follow up. This updating is achieved by directly fitting models for …

In medical research, predicting the probability of a time-to-event outcome is often of interest.
Along with failure time data, we may longitudinally observe disease markers that can …

Prediction of cause-specific cumulative incidence function (CIF) is of primary interest to
clinical researchers when conducting statistical analysis involving competing risks. The …

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 …