In biomedical studies it is common to collect data on multiple biomarkers during study follow‐
up for dynamic prediction of a time‐to‐event clinical outcome. The biomarkers are typically …

Dynamic prediction of the risk of a clinical event by using longitudinally measured
biomarkers or other prognostic information is important in clinical practice. We propose a …

In many studies, sequence of events may occur over time that produce repeated measures
with censored observations. Multi-state models are commonly used, and the effect of risk …

Background In the development of prediction models for a clinical event, it is common to use
the static prediction modeling (SPM), a regression model that relates baseline predictors to …

The importance of developing personalized risk prediction estimates has become
increasingly evident in recent years. In general, patient populations may be heterogenous …

With the rapidly increasing availability of data in the public domain, combining information
from different sources to infer about associations or differences of interest has become an …

Patients with cardiovascular diseases who experience disease‐related short‐term events,
such as hospitalizations, often exhibit diverse long‐term survival outcomes compared to …

Motivation Providing a dynamic assessment of prognosis is essential for improved
personalized medicine. The landmark model for survival data provides a potentially powerful …

Survival analysis is concerned with studying the time between entry to a study and a
subsequent event (such as death). Survival times now often refer to the development of a …

Simulation studies provide an important statistical tool in evaluating survival methods,
requiring an appropriate data-generating process to simulate data for an underlying …