Background Available methods for the joint modelling of longitudinal and time-to-event
outcomes have typically only allowed for a single longitudinal outcome and a solitary event …

Background In clinical research, there is an increasing interest in joint modelling of
longitudinal and time-to-event data, since it reduces bias in parameter estimation and …

We propose a semiparametric multivariate skew–normal joint model for multivariate
longitudinal and multivariate survival data. One main feature of the posited model is that we …

In clinical and epidemiological studies, when the time-to-event (s) and the longitudinal
outcomes are associated, modelling them separately may give biased estimates. A joint …

This paper presents a novel semiparametric joint model for multivariate longitudinal and
survival data (SJMLS) by relaxing the normality assumption of the longitudinal outcomes …

Typical joint modeling of longitudinal measurements and time to event data assumes that
two models share a common set of random effects with a normal distribution assumption …

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 modeling of longitudinal measurements and time-to-event data is used in many
practical studies of medical sciences. Most of the time, particularly in clinical studies and …

Background Admission to the ICU (intensive care unit) is frequently complicated by early AKI
(acute kidney injury). The development of AKI following cardiac surgery is particularly …

Background Chronic kidney disease (CKD) is a major public health problem that may lead to
end-stage renal disease (ESRD). Renal transplantation has become the treatment modality …