# JMbayes2: Extended Joint Models for Longitudinal and Time-to-Event Data

The package **JMbayes2** fits joint models for longitudinal and time-to-event data. It can accommodate multiple longitudinal outcomes of different type (e.g., continuous, dichotomous, ordinal, counts), and assuming different distributions, i.e., Gaussian, Studentâ€™s-t, Gamma, Beta, unit Lindley, censored Normal, Binomial, Poisson, Negative Binomial, and Beta-Binomial. For the event time process, right, left and interval censored data can be handled, while competing risks and multi-state processes are also covered.

**JMbayes2** fits joint models using Markov chain Monte Carlo algorithms implemented in C++. Besides the main modeling function, the package also provides a number of functions to summarize and visualize the results.

## Installation

**JMbayes2** can be installed from CRAN:

`install.packages("JMbayes2")`

The development version can be installed from GitHub:

```
# install.packages("remotes")
remotes::install_github("drizopoulos/jmbayes2")
```

## Minimal Example

To fit a joint model in **JMbayes2** we first need to fit separately the mixed-effects models for the longitudinal outcomes and a Cox or accelerated failure time (AFT) model for the event process. The mixed models need to be fitted with function `lme()`

from the **nlme** package or function `mixed_model()`

from the **GLMMadaptive** package. The Cox or AFT model need to be fitted with function `coxph()`

or function `survreg()`

from the **survival** package. The resulting model objects are passed as arguments in the `jm()`

function that fits the corresponding joint model. We illustrate this procedure for a joint model with three longitudinal outcomes using the PBC dataset:

```
# Cox model for the composite event death or transplantation
pbc2.id$status2 <- as.numeric(pbc2.id$status != 'alive')
CoxFit <- coxph(Surv(years, status2) ~ sex, data = pbc2.id)
# a linear mixed model for log serum bilirubin
fm1 <- lme(log(serBilir) ~ year * sex, data = pbc2, random = ~ year | id)
# a linear mixed model for the prothrombin time
fm2 <- lme(prothrombin ~ year * sex, data = pbc2, random = ~ year | id)
# a mixed effects logistic regression model for ascites
fm3 <- mixed_model(ascites ~ year + sex, data = pbc2,
random = ~ year | id, family = binomial())
# the joint model that links all sub-models
jointFit <- jm(CoxFit, list(fm1, fm2, fm3), time_var = "year",
n_iter = 12000L, n_burnin = 2000L, n_thin = 5L)
summary(jointFit)
```