FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data
A joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>.
The time-to-event data is modelled using a (cause-specific) Cox
proportional hazards regression model with time-fixed covariates.
The longitudinal biomarkers are modelled using a linear mixed
effects model. The association between the longitudinal submodel
and the survival submodel is captured through shared random
effects. It allows researchers to analyze large-scale data to
model biomarker trajectories, estimate their effects on event
outcomes, and dynamically predict future events from patients’
past histories. A function for simulating survival and longitudinal
data for multiple biomarkers is also included alongside built-in
datasets.
| Version: |
1.5.3 |
| Depends: |
R (≥ 3.5.0), survival, utils, MASS, statmod, magrittr |
| Imports: |
Rcpp (≥ 1.0.7), dplyr, nlme, caret, timeROC, future, future.apply, rlang (≥ 0.4.11) |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
testthat (≥ 3.0.0), spelling |
| Published: |
2025-11-08 |
| DOI: |
10.32614/CRAN.package.FastJM |
| Author: |
Shanpeng Li [aut, cre],
Ning Li [ctb],
Emily Ouyang [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 |
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