JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data
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 |
| 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 |
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