[R] [R-pkgs] New Package 'JMbayes' for the Joint Modeling of Longitudinal and Survival Data under a Bayesian approach

[D. Rizopoulos]

Dear R-users,

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.


Best,
Dimitris

-- 
Dimitris Rizopoulos
Assistant Professor
Department of Biostatistics
Erasmus University Medical Center

Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
Web: http://www.erasmusmc.nl/biostatistiek/
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