Fits the joint model proposed by Henderson and colleagues (2000) , 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 (Grant number MR/M013227/1).

Documentation

Manual: joineRML.pdf
Vignettes:

Maintainer: Graeme L. Hickey <graeme.hickey at liverpool.ac.uk>

Author(s): Graeme L. Hickey*, Pete Philipson*, Andrea Jorgensen*, Ruwanthi Kolamunnage-Dona*, Paula Williamson*, Dimitris Rizopoulos* (data/renal.rda, R/hessian.R, R/vcov.R)

Install package and any missing dependencies by running this line in your R console:

install.packages("joineRML")

Depends R (>= 3.3.0), nlme, survival
Imports ggplot2, graphics, lme4(>=1.1-8), MASS, Matrix, mvtnorm, Rcpp(>=0.12.7), stats, utils
Suggests JM, joineR, knitr, rmarkdown, testthat
Enhances
Linking to Rcpp, RcppArmadillo
Reverse
depends
Reverse
imports
Reverse
suggests
Reverse
enhances
Reverse
linking to

Package joineRML
Materials
URL https://github.com/graemeleehickey/joineRML/
Task Views
Version 0.3.0
Published 2017-07-23
License GPL-3 | file LICENSE
BugReports https://github.com/graemeleehickey/joineRML/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks joineRML check results
Package source joineRML_0.3.0.tar.gz