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, Student’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, multi-state
and recurrent-event 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 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)
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