[R] [R-pkgs] package JM -- version 0.8-0
d.rizopoulos at erasmusmc.nl
Wed Dec 15 12:21:06 CET 2010
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 a 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:
* for all joint models fitted by JM there is now the option to use a
pseudo adaptive Gauss-Hermite rule. This is much faster than the default
option and produces results of equal or better quality. It can be
invoked via the 'method' argument of jointModel() by specifying "aGH"
instead of "GH", e.g., 'method = "piecewise-PH-aGH"' instead of 'method
* function rocJM() has been added that estimates time-dependent
sensitivity and specificity (and the corresponding time-dependent
ROCs and AUCs) for longitudinal markers under the joint modeling
framework. The function also allows for several predictions rules.
* the new argument 'interFact' added in jointModel() allows the
specification of interaction terms between the longitudinal outcome and
* the new arguments 'parameterization' and 'derivForm' added in
jointModel() allow the specification of more general association
structures between the longitudinal marker and the risk for an event.
For instance, if a random intercepts and random slopes mixed model has
been postulated for the longitudinal outcome, then this argument can be
used to also associate the risk for an event with the subject-specific
* a predict method has been added. Currently this only calculates fitted
average longitudinal evolutions based on the information provided in the
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.
Department of Biostatistics
Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
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