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.
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
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
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.
Maintainer: Dimitris Rizopoulos <email@example.com>
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.
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