Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues (single event time) and by Williamson and colleagues (2008) (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).

Documentation

Manual: joineR.pdf
Vignettes:

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

Author(s): Pete Philipson*, Ines Sousa*, Peter J. Diggle*, Paula Williamson*, Ruwanthi Kolamunnage-Dona*, Robin Henderson*, Graeme L. Hickey*, Maria Sudell*

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

install.packages("joineR")

Depends R (>= 3.1), survival
Imports graphics, lattice, MASS, nlme, statmod, stats, utils
Suggests knitr, rmarkdown, testthat, covr
Enhances
Linking to
Reverse
depends
Reverse
imports
Reverse
suggests
joineRML
Reverse
enhances
Reverse
linking to

Package joineR
Materials
URL https://github.com/graemeleehickey/joineR/
Task Views Survival
Version 1.2.0
Published 2017-05-19
License GPL-3 | file LICENSE
BugReports https://github.com/graemeleehickey/joineR/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks joineR check results
Package source joineR_1.2.0.tar.gz