JMbayes: Joint Modeling of Longitudinal and Time-to-Event Data in R...

Description Details Author(s) References See Also


This package fits shared parameter models for the joint modeling of normal longitudinal responses and event times under a Bayesian approach. Various options for the survival model and the association structure are provided.


Package: JMbayes
Type: Package
Version: 0.8-85
Date: 2020-01-08
License: GPL (>=2)

The package has a single model-fitting function called jointModelBayes, which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a Cox model object fit returned by function coxph() of package survival. The survMod argument of specifies the type of survival submodel to be fitted; available options are a relative risk model with a Weibull baseline hazard (default) and a relative risk model with a B-spline approximation of the log baseline risk function. In addition, the param specifies the association structure between the longitudinal and survival processes; available options are: "td-value" which is the classic formulation used in Wulfsohn and Tsiatis (1997); "td-extra" which is a user-defined, possibly time-dependent, term based on the specification of the extraForm argument of jointModelBayes. This could be used to include terms, such as the time-dependent slope (i.e., the derivative of the subject-specific linear predictor of the linear mixed model) and the time-dependent cumulative effect (i.e., the integral of the subject-specific linear predictor of the linear mixed model); "td-both" which is the combination of the previous two parameterizations, i.e., the current value and the user-specified terms are included in the linear predictor of the relative risk model; and "shared-RE" where only the random effects of the linear mixed model are included in the linear predictor of the survival submodel.

The package also offers several utility functions that can extract useful information from fitted joint models. The most important of those are included in the See also Section below.


Dimitris Rizopoulos

Maintainer: Dimitris Rizopoulos <>


Guo, X. and Carlin, B. (2004) Separate and joint modeling of longitudinal and event time data using standard computer packages. The American Statistician 54, 16–24.

Henderson, R., Diggle, P. and Dobson, A. (2000) Joint modelling of longitudinal measurements and event time data. Biostatistics 1, 465–480.

Rizopoulos, D. (2016). The R package JMbayes for fitting joint models for longitudinal and time-to-event data using MCMC. Journal of Statistical Software 72(7), 1–45. doi:10.18637/jss.v072.i07.

Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Rizopoulos, D. (2011) Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics 67, 819–829.

Rizopoulos, D. and Ghosh, P. (2011) A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statistics in Medicine 30, 1366–1380.

Rizopoulos, D., Verbeke, G. and Molenberghs, G. (2010) Multiple-imputation-based residuals and diagnostic plots for joint models of longitudinal and survival outcomes. Biometrics 66, 20–29.

Tsiatis, A. and Davidian, M. (2004) Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 14, 809–834.

Wulfsohn, M. and Tsiatis, A. (1997) A joint model for survival and longitudinal data measured with error. Biometrics 53, 330–339.

See Also

jointModelBayes, survfitJM, aucJM, dynCJM, prederrJM, predict.JMbayes, logLik.JMbayes

JMbayes documentation built on Jan. 9, 2020, 9:07 a.m.