(by Dimitris Rizopoulos) Dear R-users, I’d like to announce the release of version 1.0-0 of package JM (already 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. Some basic features of JM: * it fits joint models for continuous longitudinal responses and allows for several options for the survival submodel, including PH models with Weibull, piecewise-constant, spline-approximated and unspecified baseline hazard functions. The most complete option is the PH model with the spline-approximated baseline hazard, which also allows for the inclusion of stratification factors, competing risks and (exogenous) time-varying covariates; * it allows for several formulations of the association structure between the longitudinal and survival outcomes; * it computes dynamic individualized predictions for the survival and longitudinal outcomes, which are updated as extra longitudinal information is recorded; * it computes time-dependent sensitivity and specificity, and the corresponding ROCs and AUCs with several options for the prediction rule; * several types of residuals are supported for both outcomes; * fast fitting of these models is facilitated with a pseudo-adaptive Gaussian-Hermite rule. The theory and application of this type of models along with a comprehensive overview of the capabilities of the package can be found in the recently published book “Joint Models for Longitudinal and Time-to-Event Data, with Applications in R” by Chapman and Hall/CRC (http://www.crcpress.com/product/isbn/9781439872864). The code used in the book and additional material are available in the R-forge web site: http://jmr.r-forge.r-project.org/ Additional information can be found in the corresponding help files, and examples at the R wiki web page devoted to JM: 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