FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data
Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data
applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is
modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal
outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model
is estimated using an Expectation Maximization algorithm.
| Version: |
1.2.0 |
| Depends: |
R (≥ 3.5.0), MASS, statmod |
| Imports: |
Rcpp (≥ 1.0.7), survival, dplyr, nlme, mvtnorm, Hmisc |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
testthat (≥ 3.0.0), spelling |
| Published: |
2022-08-06 |
| Author: |
Shanpeng Li [aut, cre],
Ning Li [ctb],
Hong Wang [ctb],
Jin Zhou [ctb],
Hua Zhou [ctb],
Gang Li [ctb] |
| Maintainer: |
Shanpeng Li <lishanpeng0913 at ucla.edu> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Materials: |
README NEWS |
| CRAN checks: |
FastJM results |
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