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

A key question in clinical practice is accurate prediction of patient prognosis. To this end,
nowadays, physicians have at their disposal a variety of tests and biomarkers to aid them in …

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 recent years, a wide range of markers have become available as potential tools to predict
risk or progression of disease. In addition to such biological and genetic markers, short-term …

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 it is often of interest to investigate how a marker that is repeatedly
measured in time is associated with a time to an event of interest. This type of research …

Long-term follow-up is common in many medical investigations where the interest lies in
predicting patients' risks for a future adverse outcome using repeatedly measured predictors …

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 …

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 …

The problem of dynamic prediction with time‐dependent covariates, given by biomarkers,
repeatedly measured over time, has received much attention over the last decades. Two …