JMbayes2.RdFit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated.
| Package: | JMbayes2 |
| Type: | Package |
| Version: | 0.5-1 |
| Date: | 2024-06-05 |
| License: | GPL (>=3) |
This package fits joint models for longitudinal and time-to-event data. It can accommodate multiple longitudinal outcomes of different type (e.g., continuous, dichotomous, ordinal, counts), and assuming different distributions, i.e., Gaussian, Student's-t, Gamma, Beta, unit Lindley, censored Normal, Binomial, Poisson, Negative Binomial, and Beta-Binomial. For the event time process, right, left and interval censored data can be handled, while competing risks and multi-sate processes are also covered.
JMbayes2 fits joint models using Markov chain Monte Carlo algorithms implemented in C++. The package also offers several utility functions that can extract useful information from fitted joint models. The most important of those are included in the See also Section below.
Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data With Applications in R. Boca Raton: Chapman & Hall/CRC.