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 …

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

Dynamic prediction uses patient information collected during follow-up to produce
individualized survival predictions at given time points beyond treatment or diagnosis. This …

Dynamic prediction methods incorporate longitudinal biomarker information to produce
updated, more accurate predictions of conditional survival probability. There are two …

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 …

Dynamic prediction of survival data in the presence of time-varying covariates is an area of
active research. Two common analytic approaches for this type of data are joint modeling of …

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 …

Many medical decisions involve the use of dynamic information collected on individual
patients toward predicting likely transitions in their future health status. If accurate …

In survival analysis, longitudinal information on the health status of a patient can be used to
dynamically update the predicted probability that a patient will experience an event of …