Fits the joint model proposed by Henderson and colleagues (2000)
joineRML
is an extension of the joineR package for fitting joint models of time-to-event data and multivariate longitudinal data. The model fitted in joineRML is an extension of the Wulfsohn and Tsiatis (1997) and Henderson et al. (2000) models, which is comprised on (K + 1)-sub-models: a Cox proportional hazards regression model (Cox, 1972) and a K-variate linear mixed-effects model - a direct extension of the Laird and Ware (1982) regression model. The model is fitted using a Monte Carlo Expectation-Maximization (MCEM) algorithm, which closely follows the methodology presented by Lin et al. (2002).
As noted in Hickey et al. (2016), there is a lack of statistical software available for fitting joint models to multivariate longitudinal data. This is contrary to a growing methodology in the statistical literature. joineRML
is intended to fill this void.
The main workhorse function is mjoint
. As a simple example, we use the heart.valve
dataset from the package and fit a bivariate joint model.
library(joineRML)data(heart.valve)hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ] set.seed(12345)fit <- mjoint( formLongFixed = list("grad" = log.grad ~ time + sex + hs, "lvmi" = log.lvmi ~ time + sex), formLongRandom = list("grad" = ~ 1 | num, "lvmi" = ~ time | num), formSurv = Surv(fuyrs, status) ~ age, data = list(hvd, hvd), timeVar = "time")
The fitted model is assigned to fit
. We can apply a number of functions to this object, e.g. coef
, logLik
, plot
, print
, ranef
, fixef
, summary
, AIC
, getVarCov
, vcov
, confint
, sigma
, update
, and formula
. For example,
summary(fit)plot(fit, param = 'gamma')
mjoint
automatically estimates approximate standard errors using the empirical information matrix (Lin et al., 2002), but the bootSE
function can be used as an alternative.
If you spot any errors or wish to see a new feature added, please file an issue at https://github.com/graemeleehickey/joineRML/issues or email Graeme Hickey.
For an overview of the model estimation being performed, please see the technical vignette, which can be accessed by
vignette('technical', package = 'joineRML')
For a demonstration of the package, please see the introductory vignette, which can be accessed by
vignette('joineRML', package = 'joineRML')
This project is funded by the Medical Research Council (Grant number MR/M013227/1).
To install the latest developmental version, you will need R version (version 3.1 or higher) and some additional software depending on what platform you are using.
If not already installed, you will need to install Rtools. Choose the version that corresponds to the version of R that you are using.
If not already installed, you will need to install Xcode Command Line Tools. To do this, open a new terminal and run
$ xcode-select --install
To verify that the install was successful, run the following line in the terminal
$ xcode-select -p
which should return the following
/Library/Developer/CommandLineTools
The latest developmental version will not yet be available on CRAN. Therefore, to install it, you will need devtools
. You can check you are using the correct version by running
pkg_check <- require('devtools')if (pkg_check) { pkg_check <- (packageVersion("devtools") >= 1.6)}if (!pkg_check) { install.packages('devtools')}
Once the prerequisite software is installed, you can install joineRML
(without the vignettes) by running the following command in an R console
library('devtools')install_github('graemeleehickey/joineRML')
If you have LaTeX installed, you can install joineRML
(with the vignettes) by running the following command in an R console
library('devtools')install_github('graemeleehickey/joineRML', build_vignettes = TRUE)
Note that LaTeX will need the following packages: graphicx
, amsmath
, amssymb
, amsfonts
, setspace
, enumitem
, hyperref
. Note, however, that one of the vignettes requires quite a bit of time to run and compile (approx. 15 minutes), so you may wish to skip this process.
Cox DR. Regression models and life-tables. J R Stat Soc Ser B Stat Methodol. 1972; 34(2): 187-220.
Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4): 465-480.
Hickey GL, Philipson P, Jorgensen A, Kolamunnage-Dona R. Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues. BMC Med Res Methodol. 2016; 16(1): 117.
Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982; 38(4): 963-974.
Lin H, McCulloch CE, Mayne ST. Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables. Stat Med. 2002; 21: 2369-2382.
Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1): 330-339.