JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data (original) (raw)
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 |
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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 |
License: | GPL (≥ 3) |
NeedsCompilation: | yes |
Language: | en-US |
Materials: | README |
CRAN checks: | JMH results |
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