Dynamically predicting patient survival probabilities using longitudinal measurements has
become of great importance with routine data collection becoming more common. Many …

Joint modeling and landmark modeling are two mainstream approaches to dynamic
prediction in longitudinal studies, that is, the prediction of a clinical event using longitudinally …

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 …

Aim This study aims to investigate α‐fetoprotein (AFP) trajectories for prediction of survival
outcomes after hepatic arterial infusion chemotherapy (HAIC) treatment in large …

In modern high‐throughput plant phenotyping, images of plants of different genotypes are
repeatedly taken throughout the growing season, and phenotypic traits of plants (eg, plant …

In longitudinal cohort studies, it is often of interest to predict the risk of a terminal clinical
event using longitudinal predictor data among subjects at risk by the time of the prediction …

Predicting patient survival probabilities based on observed covariates is an important
assessment in clinical practice. These patient-specific covariates are often measured over …

High dimensional data play an ever increasing role in the study of human health and
behavior. Recent technological advances have presented researchers with increasing …