Recent advancement in technology has lead to a wide range of genetic and biological
markers that hold great potential in improving the prediction of survival outcomes. Although …

Thanks to the growing interest in personalized medicine, joint modeling of longitudinal
marker and time‐to‐event data has recently started to be used to derive dynamic individual …

The Cox model is one of the most used statistical models in medical research. It models the
hazard rate of an event and its association with covariates through hazard ratios. In the …

At the individual level we introduce the empirical rate:(d, y),—the number of events (d∈{0,
1}) during y risk time. Each individual contributes several observations. Empirical rates are …

This paper extends dynamic prediction by landmarking to recurrent event data. The
motivating data comprised post-kidney transplantation records of repeated infections and …

In cardiovascular disease (CVD) studies, the events of interest may be recurrent (multiple
occurrences from the same individual). During the study follow-up, longitudinal …

The Cox regression model is a cornerstone of modern survival analysis and is widely used
in many other fields as well. But the Cox models with time-change covariates are not easy to …

Predicting the individual risk of a clinical event using the complete patient history is still a
major challenge for personalized medicine. Among the methods developed to compute …

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 …

Individualized prediction is a hallmark of clinical medicine and decision making. However,
most existing prediction models rely on biomarkers and clinical outcomes available at a …