[R-pkgs] New Package 'JMbayes' for the Joint Modeling of Longitudinal and Survival Data under a Bayesian approach
d.rizopoulos at erasmusmc.nl
Tue Sep 18 15:39:30 CEST 2012
I would like to announce the release of the new package JMbayes
available from CRAN (http://CRAN.R-project.org/package=JMbayes). This
package fits shared parameter models for the joint modeling of normal
longitudinal responses and event times under a Bayesian approach using
JAGS, WinBUGS or OpenBUGS.
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.
* jointModelBayes() allows for joint models with relative risk survival
submodels with Weibull or B-spline approximated baseline hazard
functions (controlled by argument 'survMod').
* In addition, argument 'param' of jointModelBayes() specifies the
association structure between the longitudinal and survival processes;
available options are:
- "td-value" which is the classic joint model 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 provides functionality for computing dynamic
predictions for the longitudinal and time-to-event outcomes using
functions predict() and survfitJM(), respectively.
As always, any kind of feedback (questions, suggestions, bug-reports,
etc.) is more than welcome.
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
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