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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# joineRML <img src="man/figures/hex.png" width = "175" height = "200" align="right" />
[](https://travis-ci.org/graemeleehickey/joineRML)
[](https://ci.appveyor.com/project/graemeleehickey/joineRML)
<!--[](http://www.gnu.org/licenses/gpl-3.0.html)-->
[](https://CRAN.R-project.org/package=joineRML)
[](https://CRAN.R-project.org/package=joineRML)
[](https://CRAN.R-project.org/package=joineRML)
[](https://codecov.io/gh/graemeleehickey/joineRML)
[](http://depsy.org/package/r/joineRML)
[](https://doi.org/10.5281/zenodo.1158231)
`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).
## Why use joineRML?
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.
# Example
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.
```r
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`, `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,
```r
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.
# Errors and updates
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](mailto:graeme.hickey@liverpool.ac.uk).
# Further learning
For an overview of the model estimation being performed, please see the technical vignette, which can be accessed by
```r
vignette('technical', package = 'joineRML')
```
For a demonstration of the package, please see the introductory vignette, which can be accessed by
```r
vignette('joineRML', package = 'joineRML')
```
# Funding
This project is funded by the [Medical Research Council](http://www.mrc.ac.uk) (Grant number MR/M013227/1).
# Using the latest developmental version
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.
## Windows
If not already installed, you will need to install [Rtools](https://cran.r-project.org/bin/windows/Rtools/). Choose the version that corresponds to the version of R that you are using.
## Mac OSX
If not already installed, you will need to install Xcode Command Line Tools. To do this, open a new terminal and run
```bash
$ xcode-select --install
```
## From R
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
```r
library('devtools')
install_github('graemeleehickey/joineRML')
```
# References
1. Cox DR. Regression models and life-tables. *J R Stat Soc Ser B Stat Methodol.* 1972; **34(2)**: 187-220.
2. Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. *Biostatistics.* 2000; **1(4)**: 465-480.
3. 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.
4. Laird NM, Ware JH. Random-effects models for longitudinal data. *Biometrics.* 1982; **38(4)**: 963-974.
5. 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.
6. Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. *Biometrics.* 1997; **53(1)**: 330-339.