Randomness is intrinsic to many natural processes. It is also clear that, under certain
conditions, disorders are not associated with functionality. Several examples in which …

Joint models for longitudinal and time-to-event data simultaneously model longitudinal and
time-to-event information to avoid bias by combining usually a linear mixed model with a …

Background Modelling of longitudinal biomarkers and time-to-event data are important to
monitor disease progression. However, these two variables are traditionally analyzed …

Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and
excessive burden of hospitalizations over time relative to comparable Medicare patient …

The joint modeling of repeated measurements and time‐to‐event provides a general
framework to describe better the link between the progression of disease through …

The objective of this paper is to provide an introduction to the principles of Bayesian joint
modeling of longitudinal measurements and time-to-event outcomes, as well as model …

Joint modelling has become pervasive in analysing data from survival and longitudinal
studies. There are several technqiues on joint analyses of datasets from both studies …

Joint models have proven to be an effective approach for uncovering potentially hidden
connections between various types of outcomes, mainly continuous, time-to-event, and …

Medical time-to-event studies frequently include two groups of patients: those who will not
experience the event of interest and are said to be “cured” and those who will develop the …

Frailty models are generally used to model heterogeneity and dependence between
individuals. The distribution of the frailty variable is often assumed to be continuous …