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.
- Version1.0.3
- R version≥ 3.5.0
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Languageen-US
- Last release02/20/2024
Documentation
Team
Shanpeng Li
Gang Li
Show author detailsRolesContributorJin Zhou
Show author detailsRolesContributorHua Zhou
Show author detailsRolesContributor
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- Depends4 packages
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