Long-term follow-up is common in many medical investigations where the interest lies in
predicting patients' risks for a future adverse outcome using repeatedly measured predictors …

Recurrent events are common in clinical, healthcare, social and behavioral studies. A recent
analysis framework for potentially censored recurrent event data is to construct a censored …

Background In some clinical situations, patients experience repeated events of the same
type. Among these, cancer recurrences can result in terminal events such as death …

The Cox proportional hazards model is widely used in time-to-event analysis. Two time
scales are used in practice: time-on-study and chronological age. The former is the most …

Background Thanks to the advances in genomics and targeted treatments, more and more
prediction models based on biomarkers are being developed to predict potential benefit …

Dynamic prediction uses longitudinal biomarkers for real‐time prediction of an individual
patient's prognosis. This is critical for patients with an incurable disease such as cancer …

In this article, we suggest a new statistical approach considering survival heterogeneity as a
breakpoint model in an ordered sequence of time-to-event variables. The survival responses …

This paper considers a joint survival and mixed-effects model to explain the survival time
from longitudinal data and high-dimensional covariates. The longitudinal data is modeled …

Most studies characterize longitudinal biomarker trajectories by looking forward at them from
a commonly used time origin, such as the initial treatment time. For a better understanding of …

Epidemiologic and clinical studies routinely collect longitudinal measures of multiple
outcomes. These longitudinal outcomes can be used to establish the temporal order of …