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.