TheRPackageJMbayesfor Fitting Joint Models for Longitudinal and Time-to-Event Data Using MCMC

Abstract

Joint models for longitudinal and time-to-event data constitute an attractive modeling framework that has received a lot of interest in the recent years. This paper presents the capabilities of the R package JMbayes for fitting these models under a Bayesian approach using Markov chain Monte Carlo algorithms. JMbayes can fit a wide range of joint models, including among others joint models for continuous and categorical longitudinal responses, and provides several options for modeling the association structure between the two outcomes. In addition, this package can be used to derive dynamic predictions for both outcomes, and offers several tools to validate these predictions in terms of discrimination and calibration. All these features are illustrated using a real data example on patients with primary biliary cirrhosis.

Utility of temporal profiles of new cardio-renal and pulmonary candidate biomarkers in chronic heart failure

“…To estimate the associations between biomarker levels and survival, we applied a joint modeling (JM) prediction analysis that combines LME models for repeated measurements, and Cox survival analysis for time-to-event data [37]. For both the fixed-and random-effects parts of the LME models, linear terms were used for sampling times, and both intercepts and slopes were included in the random-effects design matrix.…”

“…All tests were two-tailed and were performed with R Statistical Software using packages nlme and JMbayes [37]. The network analysis was performed using Gephi software (https://gephi.org) and the matSpD application (https://gump.qimr.edu.au/general/ daleN/matSpD) available online.…”

Associations between community-level disaster exposure and individual-level changes in disability and risk of death for older Americans

“…Using the numeric variable $W_i^{*}$, rather than the dummy variables W iw , allowed us to fit a linear trend across wealth deciles, which was more parsimonious and resulted in little difference in model fit. The baseline hazard in the survival submodel was approximated using a parametric penalised splines-based method (21). …”

“…We took a Bayesian approach to model estimation and used the JMbayes package (21) in R 3.2.2 (22) to fit the model. JMbayes fits joint models using a Metropolis-based Markov chain Monte Carlo (MCMC) algorithm.…”

%JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses

“…Fortunately this has recently changed and several packages and procedures are already available. Three R (R Core Team 2018) packages; JM (Rizopoulos 2010), joineR (Williamson, Kolamunnage-Dona, Philipson, and Marson 2008;Philipson, Diggle, Sousa, Kolamunnage-Dona, Williamson, and Henderson 2018) and lcmm (Proust-Lima, Philipps, Diakite, and Liquet 2017a; Proust-Lima, Philipps, and Liquet 2017b); and one Stata (Stata Corp 2014) module, stjm (Crowther 2012), fit these models using maximum likelihood whereas the R package JMBayes (Rizopoulos 2016(Rizopoulos , 2017 produces Markov chain Monte Carlo simulations to approach this problem from a Bayesian perspective. All these software packages are limited to normal longitudinal data with the exception of the JMBayes R package that allows user-defined likelihood functions for the longitudinal data.…”