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 _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages