A vine copula model is a flexible high-dimensional dependence model which uses only
bivariate building blocks. However, the number of possible configurations of a vine copula …

Longitudinal and survival sub-models are two building blocks for joint modelling of
longitudinal and time-to-event data. Extensive research indicates separate analysis of these …

Accurate prediction of an insurer's outstanding liabilities is crucial for maintaining the
financial health of the insurance sector. We aim to develop a statistical model for insurers to …

In longitudinal studies, it is of interest to investigate how repeatedly measured markers are
associated with time to an event. Joint models have received increasing attention on …

In this article, we propose a two‐level copula joint model to analyze clinical data with
multiple disparate continuous longitudinal outcomes and multiple event‐times in the …

Deficiency of micronutrients is considered as the basic cause of health issues. There are a
large number of micronutrients to be considered for good health, which are analyzed …

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

There is an increasing interest in the use of joint models for the analysis of longitudinal and
survival data. While random effects models have been extensively studied, these models …

Joint modeling of zero-inflated count and time-to-event data is usually performed by
applying the shared random effect model. This kind of joint modeling can be considered as a …

Joint modelling of longitudinal and time-to-event data is usually described by a joint model
which uses shared or correlated latent effects to capture associations between the two …