… models with multiple longitudinal outcomes. Finally, as an alternative to … joint model we
present the latent class joint model, which assumes that the association between the longitudinal …

… application of joint models of longitudinal and time-to-event … has concentrated on a single
longitudinal outcome and a single … once a parametric distribution for r 0 r ( t ) is specified, which …

… The aim of this paper is to provide an overview of recent methodological developments
and applications of joint models for time-to-event data and multivariate longitudinal data (MVJMs)…

… we introduce the R package JMbayes that fits joint models under a … time-to-event can be
fitted in JMbayes. In this section we will illustrate how joint models for other types of longitudinal …

… In this paper we present the R package JM that fits joint models for longitudinal and time-to-event
data. … , and we present the R package JM (available from CRAN at http://CRAN.…

… joint modeling of longitudinal data and time-to-event data. Although our examples focus mostly
on biomedical applications, … R.Beran. Nonparametric regression with randomly censored …

… outcomes with a time-to-event outcome using the R interface to the Stan C++ library for
Bayesian estimation. Various association structures may be selected, and functions for dynamic …

… , however in this case we would currently recommend that the R packages joineR [29],
JM [28], or frailtypack [38] are used, which are optimized for the univariate case and exploits …

… of the joint modeling framework for longitudinal and time-to-event … We discuss first the
longitudinal submodel, then the time-to-event … options to associate the two within the joint model. …

… , are: Both components of the joint model will include terms for measured covariates and …
in this paper, we used the R packages nlme 40 and survival 41 for longitudinal and survival …