person("Maria", "Sudell", email = "m.e.sudell@liverpool.ac.uk",
Description: Analysis of repeated measurements and time-to-event data via random
effects joint models. Fits the joint models proposed by Henderson and colleagues
<doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and
colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a
single continuous repeated measure. The time-to-event data is modelled using a
(cause-specific) Cox proportional hazards regression model with time-varying
covariates. The longitudinal outcome is modelled using a linear mixed effects
model. The association is captured by a latent Gaussian process. The model is
estimated using am Expectation Maximization algorithm. Some plotting functions
and the variogram are also included. This project is funded by the Medical
Research Council (Grant numbers G0400615 and MR/M013227/1).
Packaged: 2017-05-19 19:35:05 UTC; graemeleehickey

Packaged: 2017-07-24 19:44:45 UTC; graemeleehickey

Packaged: 2017-09-14 15:43:37 UTC; graemeleehickey

person("Medical Research Council", role = "fnd",
comment = "Grant numbers: G0400615 and MR/M013227/1")
Packaged: 2018-02-06 11:17:42 UTC; graemeleehickey
Author: Pete Philipson [aut] (<https://orcid.org/0000-0001-7846-0208>),
Medical Research Council [fnd] (Grant numbers: G0400615 and

Author: Pete Philipson [aut, cre] (<https://orcid.org/0000-0001-7846-0208>),

person("Pete", "Philipson", email = "peter.philipson1@newcastle.ac.uk",

person("Graeme L.", "Hickey", email = "graemeleehickey@gmail.com",

Title: Joint Modelling of Multivariate Longitudinal Data and
person("Andrea", "Jorgensen", email = "aljorgen@liverpool.ac.uk",
Description: Fits the joint model proposed by Henderson and colleagues (2000)
<doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple
continuous longitudinal measures. The time-to-event data is modelled using a
Cox proportional hazards regression model with time-varying covariates. The
multiple longitudinal outcomes are modelled using a multivariate version of the
Laird and Ware linear mixed model. The association is captured by a multivariate
latent Gaussian process. The model is estimated using a Monte Carlo Expectation
Maximization algorithm. This project is funded by the Medical Research Council
Imports: graphics, MASS, Matrix, nlme, Rcpp (>= 0.12.7), stats,
Suggests: joineR, knitr, rmarkdown, R.rsp
Packaged: 2016-12-27 00:07:22 UTC; graemeleehickey