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 …

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 …

Time-dependent Cox regression and landmarking are the two most commonly used
approaches for the analysis of time-dependent covariates in time-to-event data. The …

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 …

In this paper, the generalized log-gamma regression model is modified to allow the
possibility that long-term survivors may be present in the data. This modification leads to a …

The maximum likelihood approach to jointly model the survival time and its longitudinal
covariates has been successful to model both processes in longitudinal studies. Random …

This paper reviews some ideas and techniques which appear central in the statistical
modelling of longitudinal observational data. The presentation is based entirely on the …

Time‐dependent covariates are frequently encountered in regression analysis for event
history data and competing risks. They are often essential predictors, which cannot be …

Data related to life histories of individuals can be obtained in many different ways, and the
usefulness of multi‐state models for statistical analysis is generally highly dependent on the …

It is common in longitudinal studies to collect information on the time until a key clinical
event, such as death, and to measure markers of patient health at multiple follow-up times …