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-sate 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 developments 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)