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: |
1.4.2 |
Depends: |
R (≥ 3.5.0), statmod, MASS |
Imports: |
Rcpp (≥ 1.0.7), dplyr, nlme, caret, survival, timeROC |
LinkingTo: |
Rcpp, RcppEigen |
Suggests: |
testthat (≥ 3.0.0), spelling |
Published: |
2024-03-01 |
DOI: |
10.32614/CRAN.package.FastJM |
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 |
Documentation:
Downloads:
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