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 …

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 …

Aims To investigate types of symptom clusters in patients with hepatocellular carcinoma after
partial hepatectomy and explore symptom cluster trajectories over time. Design A …

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 …

Many clinical trials generate both longitudinal biomarker and time‐to‐event data. We might
be interested in their relationship, as in the case of tumor size and overall survival in …

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 …

Over the last 20 or more years a lot of clinical applications and methodological development
in the area of joint models of longitudinal and time-to-event outcomes have come up. In …

Generalized linear mixed effect models (GLMEMs) are widely applied for the analysis of
correlated non-Gaussian data such as those found in longitudinal studies. On the other …

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 …