`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 of
((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)
<- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ]
hvd
set.seed(12345)
<- mjoint(
fit 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`

,
`formula`

, `resid`

, and `fitted`

. In
addition, several special functions have been added, including
`dynSurv`

, `dynLong`

, and `baseHaz`

, as
well as plotting functions for objects inheriting from the
`dynSurv`

, `dynLong`

, `ranef`

, and
`mjoint`

functions. 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 3.3.0 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`

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

Once the prerequisite software is installed, you can install
`joineRML`

by running the following command in an R
console

```
library('devtools')
install_github('graemeleehickey/joineRML')
```

`broom`

Tidiers methods for objects of class `mjoint`

(i.e. models
fit with `joineRML`

) are included in the `broom`

package; this provides methods that allow extracting model estimates,
predictions, and comparing models in a straightforward way.

See
`vignette(topic = "joineRML-broom", package = "joineRML")`

for further details and examples.

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.