Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint modeling of longitudinal and
time-to-event data using shared parameter models. These models are
applicable in mainly two settings. First, when focus is in the survival
outcome and we wish to account for the effect of an endogenous (aka
internal) time-dependent covariate measured with error. Second, when
focus is in the longitudinal outcome and we wish to correct for
nonrandom dropout.
New features include:
* jointModel() with option "spline-PH-aGH" for the 'method'
argument can
now also handle competing risks settings.
* jointModel() with option "spline-PH-aGH" for the 'method'
argument can
now also handle exogenous time-dependent covariates, using the (start,
stop] notation of coxph().
* The predict method allows now for both marginal and subject-specific
predictions for the longitudinal outcome.
* The pseudo adaptive Gauss-Hermite rule is now used by default.
More information can be found in the corresponding help files, and
examples at http://rwiki.sciviews.org/doku.php?id=packages:cran:jm
As always, any kind of feedback (e.g., 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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