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Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and longitudinal data in the presence of heterogeneous within-subject variability, proposed by Li and colleagues (2023) <doi:10.48550/arXiv.2301.06584>. The proposed method models the within-subject variability of the biomarker and associates it with the risk of the competing risks event. The time-to-event data is modeled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modeled using a mixed-effects location and scale model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.
| Version: | 1.0.3 |
| Depends: | R (≥ 3.5.0), survival, nlme, utils, MASS, statmod |
| Imports: | Rcpp (≥ 1.0.7), parallel, dplyr, stats, caret, timeROC |
| LinkingTo: | Rcpp, RcppEigen |
| Suggests: | testthat (≥ 3.0.0), spelling |
| Published: | 2024-02-20 |
| DOI: | 10.32614/CRAN.package.JMH |
| Author: | Shanpeng Li [aut, cre], 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 |
| CRAN checks: | JMH results |
| Reference manual: | JMH.pdf |
| Package source: | JMH_1.0.3.tar.gz |
| Windows binaries: | r-devel: JMH_1.0.3.zip, r-release: JMH_1.0.3.zip, r-oldrel: JMH_1.0.3.zip |
| macOS binaries: | r-release (arm64): JMH_1.0.3.tgz, r-oldrel (arm64): JMH_1.0.3.tgz, r-release (x86_64): JMH_1.0.3.tgz, r-oldrel (x86_64): JMH_1.0.3.tgz |
| Old sources: | JMH archive |
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