FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data (original) (raw)
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 |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| Language: | en-US |
| Materials: | README NEWS |
| CRAN checks: | FastJM results |
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