FastJM
Semi-Parametric Joint Modeling of Longitudinal and Survival Data
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.
- Version1.4.2
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Languageen-US
- Last release03/01/2024
Documentation
Team
Shanpeng Li
Ning Li
Show author detailsRolesContributorGang Li
Show author detailsRolesContributorHong Wang
Show author detailsRolesContributorJin Zhou
Show author detailsRolesContributorHua Zhou
Show author detailsRolesContributor
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- Depends2 packages
- Imports6 packages
- Suggests2 packages
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- Reverse Imports1 package