Changes in version: JM_1.4-7 * Changed the definition of JM:::rmvt() to work under the shifted method (noted by Graeme Hickey). * Resolved issue with non-ordered id numbers in the longitudinal submodel. ============================== Changes in version: JM_1.4-6 * Updated aucJM() method. ============================== Changes in version: JM_1.4-5 * Fixed a bug in fixef.jointModel(). ============================== Changes in version: JM_1.4-4 * Fixed a bug in phGH.fit(). ============================== Changes in version: JM_1.4-3 * Small updates. ============================== Changes in version: JM_1.4-2 * Small updates. ============================== Changes in version: JM_1.3-0 * Several minor improvements. ============================== Changes in version: JM_1.2-0 * the new generic function aucJM() calculates time-dependent AUCs for joint models. * an updated version of function dynCJM() calculates a dynamic discrimination index (weighted average of time-dependent AUCs) for joint models. * the new generic function prederrJM() calculates prediction errors for joint models. * survfitJM() is now a generic function with a method for 'jointModel' objects. * new versions of functions ins() and ibs() with updated 'weight.fun' argument, and makepredictcall() methods. ============================== Changes in version: JM_1.1-0 * a small bug has been corrected in the plot() method for 'jointModel' objects, when method = "piecewise-PH-aGH" or method "piecewise-PH-GH" was used. ============================== Changes in version: JM_1.0-1 * use of globalVariables() in source code. * a small bug has been corrected in the plot() method for 'jointModel' objects, when a random intercepts linear mixed model was used ============================== Changes in version: JM_1.0-0 * This is the version of the package related to the book: Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman & Hall/CRC. * functions dns(), dbs(), ins() and ibs() calculate numerically derivative and integrals for functions ns() and bs(), respectively. * a coef() method has been added for objects of class 'summary.jointModel'. ============================== Changes in version: JM_0.9-2 * prediction.jointModel() can also compute now prediction intervals. * rocJM() has a new logical argument 'directionSmaller' denoting whether smaller values for the longitudinal outcome are associated with higher risk for an event. * fitted.jointModel() has the new option 'Slope' for the 'type' argument that returns the fitted values corresponding to the slope term when parameterization %in% c('slope', 'both') in jointModel(). ============================== Changes in version: JM_0.9-1 * minor bug fixes. ============================== Changes in version: JM_0.9-0 * jointModel() can now also handle exogenous time-dependent covariates when method = "spline-PH-aGH". * jointModel() can now also handle competing risks settings when method = "spline-PH-aGH". * new function crLong() expands a data frame in the long format in the competing risks setting. * predict() method now calculates marginal and subject-specific predictions for the longitudinal outcome. ============================== Changes in version: JM_0.8-4 * method ranef() has now the extra argument 'type' the specifies whether to compute the mean (default) or the mode of the posterior distribution of the random effects. * the anova() method now also produces marginal Wald tests when a single joint model is provided. * plot.survfitJM() produces a more informative plot when argument 'include.y' is set to TRUE. * a bug has been corrected in residuals.jointModel() that it did not work when 'MI = TRUE', and 'parameterization = "slope"' in jointModel(). ============================== Changes in version: JM_0.8-3 * the default method is now the Weibull model under the relative risk parameterization using the pseudo-adaptive Gauss-Hermite rule. * the plot() method has a new logical argument called 'return', which if set to TRUE the values use to create the plot are returned. * a typo in the code creating the scaling for the pseudo-adaptive Gauss-Hermite points has been corrected. This was primarily affecting the standard errors in the longitudinal submodel. The point estimates may also slightly change in some datasets. ============================== Changes in version: JM_0.8-2 * a bug was corrected in the internal function ModelMats(). ============================== Changes in version: JM_0.8-1 * the new function xtable.jointModel() in conjunction with the xtable package can be used to produce a LaTeX table with the results of joint modeling analysis. * the new function simulateJM() and the simulate() method for objects of class 'jointModel' can be used to simulate data from a joint model. ============================== Changes in version: JM_0.8-0 * the new argument 'interFact' added in jointModel() allows the specification of interaction terms between the longitudinal outcome and baseline covariates. * for all joint models fitted in 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. * a predict() method has been added. Currently this only calculates fitted average longitudinal evolutions based on the information provided in the 'newdata' argument. * a new algorithm for calculating the starting values has been implemented. In most of the cases these will be closer to the MLEs than in the previous version. * some small changes have been made in the default Gauss-Hermite quadrature rule. This will result in minor changes in parameter estimates, standard errors and log-likelihood value compared to the previous version. * a bug has been corrected in the code used to specify the design matrix for the random effects in the longitudinal outcome, that did not allow this matrix not to be a subset of the design matrix of the fixed effects. ============================== Changes in version: JM_0.7-0 * the new function rocJM() has been added that calculates time-dependent ROC curves and the corresponding AUCs for joint models. * methods "weibull-AFT-GH", "weibull-PH-GH", "piecewise-PH-GH", and "spline-PH-GH" support now the true slope parameterization. This is invoked be specifying the 'parameterization' and 'derivForm' arguments accordingly. ============================== Changes in version: JM_0.6-2 * a small bug was corrected in summary.jointModel(). ============================== Changes in version: JM_0.6-1 * jointModel() has now the extra argument 'scaleWB' that allows to fix the scale parameter for the Weibull baseline hazard to a specific value. ============================== Changes in version: JM_0.6-0 * method = "spline-PH-GH" allows now to include stratification factors for which different spline coefficients are estimated. By default the knots positions are the same across strata -- this can be changed by either directly specifying the knots or by setting the control argument 'equal.strata.knots' to FALSE. * the new function wald.strata() can be used to test for equality of the spline coefficients among strata. * a confint() method has been introduced for 'jointModel' objects. * jointModel() has now the extra argument 'lag' that allows for lagged effects in the time-dependent covariate represented by the linear mixed model. * a bug was corrected in joint models with piecewise constant baseline risk function. In particular, the 'xi' parameters were reported as double their actual value. ============================== Changes in version: JM_0.5-0 * function dynC() has been added that calculates a dynamic concordance index for joint models. * method = "ch-GH" has been replaced by method = "spline-PH-GH" that fits a relative risk model with a B-spline-approximated baseline risk function. * method = "ph-GH" that fits a relative risk with an unspecified baseline risk function has been renamed to method = "Cox-PH-GH". ============================== Changes in version: JM_0.4-0 * function survfitJM() has been added that calculates predictions of subject-specific probabilities of survival given a history of longitudinal responses. * the multiple-imputation residuals now work also for joint models with piecewise constant baseline risk functions. * faster optimization algorithms have implemented for 'method = "weibull-PH-GH"' and 'method = "piecewise-PH-GH". ============================== Changes in version: JM_0.3-0 * the Weibull model is now available under both the relative risk and accelerated failure time parameterizations. * a number of enhancements have been implemented in the functions that compute the MI-based residuals. * new more robust algorithms have been written for the numerical approximation of integrals; this will lead to some discrepancies in the results, especially in the survival part, compared to the previous versions of the package. ============================== Changes in version: JM_0.2-1 * changes in e-mail addresses in .Rd files. ============================== Changes in version: JM_0.2-0 * the jointModel method for the residuals generic has further options: (i) MI residuals for fixed and random visit times for the longitudinal process, and (ii) martingale, Cox-Snell, and AFT residuals for the survival process. * Function weibull.frailty() is introduced (along with supporting methods) for fitting multivariate survival data using the Weibull model with Gamma multiplicative frailties under maximum likelihood. * several typos have been corrected in .Rd files. ============================== Changes in version: JM_0.1-1 * corrected some typos in .Rd files.