jm() now allows for zero-correlations constraints in
the covariance matrix of the random effects. When the mixed models
provided in the Mixed_objects argument have been fitted
assuming a diagonal matrix for the random effects, this will also be
assumed in the joint model (in previous versions, this was ignored). In
addition, the new argument which_independent can be used to
specify which longitudinal outcomes are to be assumed
independent.
jm() can fit joint models with a combination of
interval-censored data and competing risks (e.g., one of the the
competing events is interval-censored and the other(s) not).
A bug in the predict() method causing low AUC values
has been corrected.
The time-varying ROC and AUC now allow to correct for censoring
in the interval Tstart to Thoriz using inverse
probability of censoring weighting. The default remains model-based
weights.
Portable implementation of parallel computing.
function area() has gained the argument
time_window that specifies the window of integrating the
linear predictor of the corresponding longitudinal outcome.
Function tvBrier() has gained the argument
integrated for calculating the integrated Brier
score.
Function tvBrier() has gained the argument
type_weights and now also allows to correct for censoring
in the interval Tstart to Thoriz using inverse
probability of censoring weighting. The default remains model-based
weights.
The new function tvEPCE() calculates the
time-varying expected predictive cross-entropy.
This version supports Super Learning for optimizing predictions
using cross-validation and a library of joint models. In that regard,
the new function create_folds() can be used to split a
dataset in V-folds of training and test datasets. More information can
be found in the corresponding vignette.
Weak informative priors are now used for the fixed-effects of the mixed-effects models.
Several improvements in various internal functions.
Dynamic predictions for competing risks data can now be computed. An example is given in the Competing Risks vignette.
Function jm() can now fit joint models with a
recurrent event process with or without a terminating event. The model
accommodates discontinuous risk intervals, and the time can be defined
in terms of the gap or calendar timescale. An example is given in the
Recurrent Events vignette.
Added the function tvBrier() for calculating
time-varying Brier score for fitted joint models. Currently, only
right-censored data are supported.
Added the functions calibration_plot() and
calibration_metrics() for calculating time-varying
calibration plot and calibration metrics for fitted joint models.
Currently, only right-censored data are supported.
Added new section in the vignette for Dynamic Prediction (available on the website of the package) to showcase the use of the functions mentioned above.
Improved the plot method for dynamic predictions.
Several bug corrections.
Added a predict() method for jm objects
and a corresponding plot() for objects of class
predict_jm for calculating and displaying predictions from
joint models. Currently, only standard survival models are covered.
Future versions will include predictions from competing risks and
multi-state models.
Added the functions tvROC() and tvAUC()
for calculating time-varying Receiver Operating Characteristic (ROC)
curves and the areas under the ROC curves for fitted joint models.
Currently, only right-censored data are supported.
Added a vignette (available on the website of the package) to explain how (dynamic) predictions are calculated in the package.
Added two vignettes (available on the website of the package) to showcase joint models with competing risks and joint models with non-Gaussian longitudinal outcomes.
Simplified syntax and additional options for specifying transformation functions of functional forms.
The slope() function has gained two new arguments,
eps and direction. This allows calculating the
difference of the longitudinal profile over a user-specified
interval.
parallel::clusterSetRNGStream() in
jm_fit() for distributing the seed in the workers.floor() in the C++ code.