Dear R-users,
I'd like to announce the release of the new version of package JM (soon
available from CRAN) for the joint modelling 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
time-to-event 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:
* a relative risk model with a piecewise-constant baseline risk
function is now available for the event outcome, using option
'piecewise-PH-GH' in the 'method' argument of jointModel().
* several types of residuals are supported for the longitudinal and
time-to-event outcomes. Moreover, for the longitudinal outcome there is
also the option to compute multiple-imputation-based residuals, as
described in Rizopoulos, Verbeke and Molenberghs (Biometrics 2009, to
appear).
* the Weibull submodel for the time-to-event outcome is now available
under both the relative risk and accelerated failure time formulations.
* this new version of the package features new and more robust
algorithms for numerical integration and optimization -- these updates
could lead to different results, epsecially for the survival part
compared to the previous version the package.
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
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